Billing information just got more informative and organized
The following improvements have been made on the Billing page available to Enterprise and Division administrators:
- The Billing page has been redesigned and now consists of three sections: Billing information, Instance limits and Payment information.
- The start date from which costs are calculated is now displayed for the current billing period.
- Additional charges and credits information is now renamed to Charges and Refunds and grouped in the Additional subsection.
- Total Platform charges now sum up costs for Analysis, Storage and Additional charges.
Recently published apps
SBG Image Processing Toolkit
SBG Image Processing Toolkit consists of apps that enable various stages of machine learning image processing. Seamless integration between the tools of this toolkit provides an easy and logical analysis flow, while enabling support of various data types, preprocessing steps and utilizing computation capabilities of the Seven Bridges Platform.
- SBG Deep Learning Image Classification Exploratory Workflow is an image classifier pipeline that relies on the transfer learning approach. This allows the use of pre-trained models as the starting point for building a model adjusted to given image datasets. Furthermore, the pipeline allows training of the model for a variety of hyperparameter combinations in parallel by utilizing multiple GPU instances, while detailed metrics and visualizations help determine the best configuration that can later be used to make predictions on new data instances.
- SBG Deep Learning Prediction is an image classifier tool that classifies unlabeled images based on labeled data. It is intended as a final step after the SBG Deep Learning Image Classification Exploratory Workflow. Testing different configurations in parallel with the exploratory workflow and finding the best model configuration for the given dataset, then using SBG Deep Learning Prediction with that configuration and all available labeled images as the training data provides the optimal training conditions which lead to the best classification results.
- SBG Histology Whole Slide Image Preprocessing takes SVS histopathology images, removes various artifacts, and outputs the desired number of best quality tiles in PNG format that consist of at least 90% tissue.
- SBG X-Ray Image Preprocessing Workflow performs the selected X-ray image enhancement algorithm: unsharp masking (UM), high-frequency emphasis filtering (HEF) or contrast limited adaptive histogram equalization (CLAHE).
- SBG Stain Normalization involves casting an array of images in the stain colors of a target image. Stain normalization is used as a histopathology image preprocessing step to reduce the color and intensity variations present in stained images obtained from different laboratories.
- SBG Medical Image Convert performs medical image format conversion. If the input data are medical images in a non-standard format (e.g. SVS, TIFF, DCM or DICOM), SBG Medical Image Convert converts them to PNG format.
- SBG Split Folders organizes an image directory into the train and test subdirectory structure. These directories are necessary inputs for SBG Deep Learning Image Classification Exploratory Workflow and SBG Deep Learning Prediction.
HistoQC is an open-source quality control tool for digital pathology slides. It performs fast quality control to not only identify and delineate artefacts but also discover cohort-level outliers (e.g., slides stained darker or lighter than others in the cohort). It outputs an interactive user interface for easy viewing and understanding of the results.
Minimac4 is a genetic imputation algorithm that can be used to impute genotypes in a genomic region starting from a reference panel in M3VCF format and pre-phased target GWAS haplotypes.
BOLT-LMM is a tool that tests the association between genotypes and phenotypes using a linear mixed model.
Recently published apps
GSEAPreranked Workflow performs Gene Set Enrichment Analysis (GSEA). It is generated with an assumption that a differential expression analysis has been done before using the DESeq2 tool which is publicly available on the Seven Bridges Platform. The GSEAPreranked Workflow consists of two tools, GSEA Input Prepare and GSEAPreranked. The GSEAPreranked tool represents a wrapper around the command-line tool that was developed by the BROAD Institute. The GSEA Input Prepare tool is based on the Python script developed by the Seven Bridges team to prepare the required input file formats for the GSEAPreranked tool.
Amazon EC2 GPU G4dn instances available on the Platform
With this update you can now use the newest Amazon EC2 GPU G4dn instances, in task executions and Data Cruncher analyses, as the industry’s most cost-effective and versatile GPU instances for deploying machine learning models.
G4dn instances feature NVIDIA T4 GPUs and custom Intel Cascade Lake CPUs, and are optimized for machine learning inference and small scale training.
NVIDIA drivers come preinstalled and optimized according to the Amazon best practice for the specific instance family and are accessible from the Docker container.
The following instances have been added:
Learn more about supported GPU instance types.
Recently published apps
ENCODE ChIP-Seq Pipeline 2 analysis studies chromatin modifications and binding patterns of transcription factors and other proteins. It combines chromatin immunoprecipitation (ChIP) assays with standard NGS sequencing. The workflow is based on ChIP-Seq 2 pipeline, developed by the ENCODE Consortium.
ENCODE ATAC-seq Pipeline performs quality control and signal processing, producing alignments and measures of enrichment. The Assay for Transposase-Accessible Chromatin followed by sequencing (ATAC-seq) experiment provides genome-wide profiles of chromatin accessibility. Briefly, the ATAC-seq method works as follows: loaded transposase inserts sequencing primers into open chromatin sites across the genome, and reads are then sequenced. The ends of the reads mark open chromatin sites. The workflow is based on the ENCODE ATAC-seq pipeline, developed by the ENCODE Consortium.
SB CLI now accepts automation start parameters as YML or JSON file
Starting from SB CLI version 0.18.0, arguments necessary for initiating a new automation run via the automations CLI can come from inside a user-provided YML or JSON file.
This allows you to maintain automation parameters inside well organized and annotated file templates, instead of providing all arguments (in particular automation inputs, settings, and secret settings) in the form of a long, opaque command line string that is difficult to read and write.
When starting a new automation, you would first need to modify the local YML/JSON file and then provide the file path as an argument to the
sb automations start
command. Read more on the Manage via CLI RHEO documentation page.
GDC Datasets version update
As of May 27, GDC datasets available through the Data Browser and the API correspond to GDC Data Release 29.0.
New Command-line Uploader released
The new Command-line (CLI) Uploader, just released as part of the existing Seven Bridges CLI tool, becomes the primary recommended tool for performing large scale uploads to the Seven Bridges Platform. The Uploader is easy to install and use, and is a resilient and performant command line application that provides users with a secure and reliable way of uploading data to the Platform.
The legacy Command line uploader will remain functional until August 2021, before being officially deprecated. Along with the legacy CLI Uploader, Desktop Uploader is also planned to be deprecated in August 2021, as Web Uploader is available through the Platform’s visual interface (since September 2020). Find out more about the new CLI Uploader in our documentation.
Recently published apps
GENESIS Update Null Model for Fast Score Test updates null model file obtained with the GENESIS Null model workflow so that it can be used in the GENESIS Single Variant Association Testing workflow in fast score mode.
CWL v1.2 available on the Seven Bridges Platform
Seven Bridges Platform now supports Common Workflow Language (CWL) version v1.2. The new version of CWL brings a major new functionality – conditional execution of workflow steps, as well as several minor features and improvements. For the detailed change log please see the CWL CommandLineTool specification and the CWL Workflow specification.
The new CWL version v1.2 is a backwards-compatible upgrade of version v1.1, meaning all v1.0 and v1.1 features are still supported in v1.2. To upgrade a v1.0 or v1.1 app to v1.2, simply edit the app and the next version you save can automatically be upgraded to v1.2. Note that upgrading a workflow CWL version to v1.2 this way will not upgrade the CWL version of the tools in the workflow.
Apps using CWL v1.0 and v1.1 versions are still supported and can be used in workflows in combination with CWL v1.2 apps.
Recently published apps
The following apps were published in CWL1.x:
- SRA Toolkit 2.10.8 – NCBI’s collection of tools and libraries for accessing data in Sequence Read Archives format (SRA).
- SRA Download and Set Metadata a workflow that allows for downloading full SRA datasets and populating any metadata information that goes with the dataset
- AnnotSV 3.0.7 – structural variant annotation and raking tool.
- IsoformSwitchAnalyzeR 1.12.0 – a tool for differential splicing analysis, it performs statistical identification of the isoform switching while comparing two sample groups.
- DRIMSeq 1.16.1 – performs differential transcript usage (DTU) analyses using Dirichlet-multinomial generalized linear models.
- DEXSeq 1.36.0 – toolkit for testing differential exon usage in comparative RNA-Seq experiments.
- Differential Exon Usage with DEXSeq 1.36.0 – a workflow constructed out of DEXSeq tools, meant for a comprehensive differential splicing analysis.