Everything you need to turn data into discoveries.
From reproducible bioinformatic pipelines to exploratory data analysis. The BioBox platform is a complete workbench for scientists to autonomously execute, analyze, interpret, and transform sequencing data into discovery.
Stop wrestling with infrastructure and tap into a massively scalable on-demand compute infrastructure to run computational pipelines easily.
Beyond Data Processing
Running a workflow is just the tip of the iceberg. Do more than just workflows with a suite of visualization, data interpretation, and tertiary analysis tools to give meaning to your data.
Data Exploration & Interpretation
Fully connected analytics taps into the massive collection of data indexed in the platform. Explore your data with rich context and find the needle in the haystack.
Experience an expressive and intuitive interface that keeps things simple and easy to use, meaning your team can spend less time reading docs, and more time getting stuff done.
Everything you need to turn data into discoveries. Choose for a growing collection of pre-built, configurable workflows to fast track research
Set of analysis pipelines that process Chromium single cell data to align reads and generate barcode matrices.
R-wrapper of Seurat toolkit. Used to QC scRNA data and perform data exploration of single cell data.
Performs differential expression against two groups of samples
Quantify abundances of transcripts from bulk RNAseq data using pseudo-alignment
This workflow maps single-end or paired-end FASTQ reads to a reference genome using the STAR (v2.7.0f) aligner.
This workflow maps paired FASTQ reads to a reference genome using the Burrows-Wheeler Aligner.
DeepVariant is a deep learning-based variant caller that takes aligned reads, produces pileup image tensors from them, classifies each tensor using a convolutional neural network.
This workflow primarily uses the Genome Analysis Toolkit (GATK v18.104.22.168) to recalibrate the base positions, determine somatic single nucleotide variants and indels via local assembly of haplotypes, and then filter those calls through GATK MuTect2 Filter function.
This workflow takes in an input bam file and runs it through the Control-FREEC workflow to call copy number variations.
Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states.
This workflow is used to call peaks in ChIP data files.
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