Biotech Research Tools Repository

Part of the Benchling platform, this tool offers automated primer design with visualization in the context of your sequences and constructs.

A comprehensive primer design tool that creates custom primers and probes for PCR, qPCR, and sequencing applications with optimized design parameters.

A widely used program for designing PCR primers, hybridization probes, and sequencing primers. It considers factors like melting temperature, GC content, and primer-dimer formation.

A web interface to Primer3 for designing PCR primers with enhanced functionality and user-friendly interface

A tool for designing target-specific primers that combines Primer3 with BLAST to ensure specificity. It helps avoid amplification of non-target sequences.

An integrated CRISPR design tool within the Benchling platform that helps researchers design, analyze, and manage CRISPR experiments.

A tool for designing CRISPR/Cas-derived RNA-guided endonucleases with reduced off-target effects. It supports various Cas proteins and PAM sequences.

A web tool for CRISPR/Cas9 and TALEN target site finding and validation. It ranks target sites based on potential off-target effects and efficiency scores.

A comprehensive tool for selecting and evaluating CRISPR/Cas9 guide sequences. It provides detailed off-target analysis and efficiency predictions.

A comprehensive CRISPR target site identification and evaluation tool that focuses on efficiency scoring and off-target analysis for multiple Cas variants.

A fast and versatile algorithm that searches for potential off-target sites of CRISPR/Cas-derived RNA-guided endonucleases

A software pipeline for the analysis of CRISPR-Cas9 genome editing outcomes from deep sequencing data. It quantifies and visualizes editing efficiency, frameshift mutations, and more.

An R package for visualizing and comparing sequence variants generated by CRISPR-Cas9 editing. It provides detailed analysis of editing outcomes and frequencies.

A revolutionary AI system developed by DeepMind that predicts protein 3D structure with unprecedented accuracy, transforming structural biology research.

A fast and user-friendly implementation of AlphaFold2 that runs efficiently on Google Colab, making protein structure prediction accessible to researchers without extensive computational resources.

A bidirectional encoder representation from transformers for DNA sequences, enabling various genomic tasks like promoter prediction and variant effect analysis.

A transformer-based model developed by DeepMind that predicts gene expression and identifies functional regions from DNA sequences with unprecedented accuracy.

A family of protein language models that learn protein representations from millions of protein sequences, enabling various downstream tasks in protein science.

A transformer-based foundation model for genomics that enables transfer learning across various genomic tasks, from gene expression prediction to cell type classification.

Foundation model for single-cell multi-omics analysis using generative AI

A machine learning tool that predicts the outcomes of cytosine and adenine base editing. It helps researchers design more precise base editing experiments.

A tool for designing prime editing guide RNAs (pegRNAs) for precise genome editing. It helps researchers design optimal pegRNAs for their target sequences.

A tool for removing technical artifacts from single-cell RNA-seq data, including ambient RNA and barcode swapping.

A set of analysis pipelines that process Chromium single-cell RNA-seq output to align reads, generate feature-barcode matrices, and perform clustering and gene expression analysis.

Analysis pipeline for single cell multiome ATAC + Gene Expression data from 10x Genomics

An automated cell type annotation tool for single-cell RNA sequencing data

A generative model for single-cell RNA sequencing data that can predict cellular responses to perturbations, including gene edits.

A deep generative model for single-cell RNA sequencing data that can be used for dimensionality reduction, clustering, and differential expression analysis.

Spatial Molecular Data Analysis in Python - a package for spatial transcriptomics and proteomics data analysis

A generative pre-trained transformer model for molecular design that can generate novel molecules with desired properties.

A transformer-based model for molecular understanding and generation that can translate between molecular structures and natural language descriptions.

A molecular visualization system for structural biology that renders and animates 3D molecular structures

Predicts secondary structures of single RNA or DNA sequences using thermodynamic modeling

A software suite for modeling macromolecular structures. It's useful for protein design, protein-protein docking, and protein structure prediction.

An open-source project that provides tools for the analysis and comprehension of high-throughput genomic data using the R programming language.

A set of freely available tools for biological computation written in Python

A generalist algorithm for cell and nucleus segmentation

A deep learning-based variant caller that uses a convolutional neural network to call genetic variants from next-generation DNA sequencing data

An open, web-based platform for accessible, reproducible, and transparent computational biomedical research. It provides tools for data analysis without programming experience.

A modern compressor for genomic files (FASTQ, SAM/BAM, VCF, FASTA, GVF, 23andMe) that provides high compression ratios and fast decompression

Multiple Alignment using Fast Fourier Transform - a multiple sequence alignment program for amino acid or nucleotide sequences

A multi-dimensional image viewer for Python - designed for browsing, annotating, and analyzing large multi-dimensional images

A Python-based toolkit for analyzing single-cell gene expression data, with preprocessing, visualization, clustering, trajectory inference and differential expression testing.

An R package designed for QC, analysis, and exploration of single-cell RNA-seq data. It provides tools for dimensionality reduction, clustering, and differential expression analysis.