Spatial Registration

QuickNII - A major challenge in data mapping is the registration and alignment of 2D image sections in a 3D context. QuickNII is a stand-alone software tool for semi-automated affine spatial registration of sectional image data to a 3D reference atlas coordinate framework and can interact with the Allen Institute Common Coordinate Framework. A key feature in the tool is the capability to generate user defined cut planes through the reference atlas by matching the orientation of the cut plane of the sectional image data. The reference atlas is transformed to match anatomical landmarks in the corresponding experimental images. In this way, the spatial relationship between experimental image and atlas is defined, without introducing distortions in the original experimental images. (QuickNII, RRID:SCR_016854)

Access QuickNII here:
https://www.nitrc.org/projects/quicknii/

Inquiries and support: support@humanbrainproject.eu

J.G. Bjaalie / Neural Systems Lab (https://www.med.uio.no/imb/english/research/groups/neural-systems/), University of Oslo

Human Brain Project (http://www.humanbrainproject.eu/)

HistoloZee 2.0 is a software tool for interactively mapping 2D and 3D molecular and anatomical histology into Common Coordinate Frameworks (CCFs). It has simple, interactive registration workflows that connect user images with CCFs. Planned features include:

Concurrent rendering of high-resolution histology and 3D images
Slicing of images at arbitrary orientations
Seamless multi-scale zooming
Surface meshes of anatomical structures
Interactive affine registration
User-guided deformable registration
Persistence of projects in human-readable format
Loading of common histology and medical image formats

Access HistoloZee 2.0 here:
‍http://picsl.upenn.edu/software/histolozee/

Penn Image Computing and Science Lab (PICSL) (http://picsl.upenn.edu/software/histolozee/), University of Pennsylvania

Cell Locator is a tool for placing tissue samples into annotated 3D coordinate systems. Users can draw polylines onto arbitrarily oriented slices of the 3D space, give them a physical thickness, and save the results to the JSON file.

This tool is intended to be used by members of the BICCN to localize their cells and tissue samples so that data can be described in a consistent spatial framework. The tool currently supports the Allen Common Coordinate Framework v3, and will be extended to support a human reference space soon.

Cell Locator was developed by Kitware, Inc. It currently supports Windows, and will soon have installers for MacOS and Linux.

Access Cell Locator here:
https://github.com/BICCN/cell-locator/

Allen Institute for Brain Science (https://alleninstitute.org/what-we-do/brain-science/)

Kitware, Inc. (https://www.kitware.com/)

SMART (semi-manual alignment to reference templates) extends the WholeBrain framework in R for segmenting and registering experimental images to the Allen Mouse Common Coordinate Framework (CCF). SMART streamlines the processing of large volumetric LSFM datasets and solves issues with non-uniform morphing across the anterior-posterior axis with an interactive “choice game.” It also accounts for duplicate cell counts in adjacent z images and presents new ways to easily parse apart and interactively visualize the final mapped datasets.

Access SMART here:
https://github.com/mjin1812/SMART

https://mjin1812.github.io/SMART/installation.html

Behavioral Neuroscience Research Branch, The National Institute on Drug Abuse Intramural Research Program (https://irp.drugabuse.gov/organization/bnrb/)

BICCN Pipelines

Overview

The Broad Institute’s Data Sciences Platform (DSP) develops production-level data processing pipelines in collaboration with multiple consortia including BICCN. Thank you to everyone who has worked with us to create and improve these pipelines. For more details please see our BICCN collaborators and how to cite the pipelines.

Pipelines are available for multiple data types hosted in the BICCN network, including single-cell and single-nucleus transcriptomics, methylomics, and ATAC-seq data.

Pipeline	WARP WDL Code	Input Data	Overview	Terra Workspace	Publication (if applicable)
MethylC-Seq (CEMBA; RRID:SCR_021219)	CEMBA	Multiplexed single-nucleus bisulfite sequencing data	CEMBA Overview	CEMBA	Luo et al. 2017
Optimus (RRID:SCR_018908)	Optimus	10x Genomics V2 and V3 3' single-cell and single-nucleus data	Optimus Overview	Optimus
Single-Cell ATAC (scATAC; RRID:SCR_018919)	scATAC	Single-cell ATAC-seq data from nuclear isolates	scATAC Overview	scATAC	Fang et al. (2021)
Smart-seq2 Single Nucleus Multi-Sample (RRID:SCR_021312)	Smart-seq2 Single Nucleus Multi-Sample	Single-cell data generated with Smart-seq2 assays	Smart-seq2 Single Nucleue Multi-Sample Overview	Smart-seq2 Single Nucleus Multi-Sample

Pipeline Standards, Maintenance, and Availability

Pipelines are cloud-optimized and developed to ensure portability as well as data reproducibility and interoperability. To this aim, the pipelines are:

Open-access and developed with GA4GH standards.
Written in the Workflow Description Language (WDL), a community-maintained, human-readable workflow language that can run on Cromwell, a portable execution engine that can be launched anywhere, locally or in the cloud.
Containerized using public Docker instances, allowing researchers to exactly reproduce the workflow software.

The pipeline code is available from multiple sources, including GitHub, Dockstore, and the BCDC cloud computing environment (Terra).

Code is developed and maintained in the WDL Analysis Research Pipelines (WARP) repository in GitHub. Overviews and workflow code for BICCN-related pipelines are linked in the table above; additionally, relevant pipelines can be identified by typing the keyword “BICCN” in the WARP Documentation search bar.The WARP Overview details navigating the repository, pipeline development, and running the workflows.
Workflows are available for export from Dockstore, a GA4GH-compliant platform for sharing Docker-based tools. Search “warp” on Dockstore to find all WARP pipelines, including those used in the BICCN.
Workflows are also available to test on Terra, the cloud-based bioinformatics platform used for BCDC data processing. To get started, register for Terra using the registration guide. To try a pipeline, navigate to the pipeline’s workspace linked in the table above or search for the “BICCN” tag in the workspaces tag search bar. Each workspace contains downsampled data, detailed instructions for using the workflows, and cost guidelines. Learn more about Terra with the Getting Started guides.

Citing the Pipelines

Each BICCN pipeline (see table above) has a SciCrunch resource identifier (RRID) that can be cited in publications. Follow the SciCrunch citation guidelines.

Example: (Optimus Pipeline, RRID:SCR_018908)

Additionally, please refer to the table above to cite any publications associated with the pipeline.

Additional Single-cell Transcriptomic Pipeline Resources

These pipelines produce outputs and quality control metrics that can be further analyzed and visualized with downstream community resources. Tutorials for combining single-cell transcriptomic data and pipelines with common community tools are available in the following resources:

BICCN Omics Workshop Workspace

This tutorial Terra workspace is a step-by-step guide to analyzing BICCN 10x Genomics single-cell data. Using this workspace, researchers learn how to:

Import an example 10x dataset (FASTQs) from NeMO
Align example 10x FASTQs and produce a raw count matrix with quality metrics using the Optimus workflow
Filter, normalize, and cluster the raw count matrix with the Cumulus workflow
Explore single-cell data in a Seurat Jupyter Notebook

BICCN Omics Workshop Webinar Recording

This is some text insideThis Brain Initiative Cell Census Network (BICCN) virtual workshop guides you through finding data in the Neuroscience Multi-Omic (NeMO) Archive, analyzing that data in Terra through workflows (pipelines) and interactive analysis, then publishing the results to a study in the Single Cell Portal (SCP). of a div block.

BICCN Omics Workshop Blog

This high-level overview of the BICCN Omics Workshop describes the BICCN Omics workshop content and provides a link to the webinar demonstration.

Acknowledgments

We thank the following BICCN collaborators and Broad Pipelines Team members for their work on these pipelines:

MethylC-Seq (CEMBA)

Our gratitude to the Joseph Ecker Lab and special thanks to Joseph Ecker, Chongyuan Luo, Eran Mukamel, Hanqing Liu, Benjamin Carlin, Dan Moran, and Jeff Korte.

Single-Cell ATAC (scATAC)

Our gratitude to the Bing Ren Lab and special thanks to Bing Ren, Rongxin Fang, Yang Li, Sebastian Preissl, Nick Barkas, and Kylee Degatano.

Smart-seq2 Single Nucleus

Our gratitude to the Allen Institute, the Eran Mukamel Lab, and the NeMO team. Special thanks to Eran Mukamel, Fangming Xie, Zizhen Yao, Changkyu Lee, Jeff Goldy, Brian Herb, Cindy van Velthoven, Carrie McCracken, Kishori Konwar, Farzaneh Khajouei, Jessica Way, and Kylee Degatano.

Optimus

Our gratitude to Alex Dobin and the Eran Mukamel Lab. Special thanks to Kishori Konwar, Farzaneh Khajouei, Jessica Way, Ambrose Carr, Jishu Xu, Jose Soto, and Nick Barkas. This pipeline is currently being updated for the BICCN; more acknowledgments will be added as the work progresses.