StanfordBioinformatics/JsonWf

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Python
License
GNU General Publi...
Created over 10 years ago
Updated over 6 years ago

StanfordBioinformatics/JsonWf

StanfordBioinformatics

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A JSON workflow framework

SJM

Simple Job Manager

loom

A tool for running bioinformatics workflows locally or in the cloud.

HugeSeq

For original Nature Biotechnology Publication (Q1 2012)

mvp_aaa_codelabs

Queries to perform qc and analysis on genomes in BigQuery

Hummingbird

Hummingbird: Efficient Performance Prediction for Executing Genomic Applications in the Cloud: https://doi.org/10.1093/bioinformatics/btab161

personal-health-dashboard

Jupyter Notebook

wearable-infection

Real-time detection of infection diseases using wearables

bigquery-genomics-qc

Genomics QC pipeline

stanford-benchmarking-workflows

trellis-mvp-functions

Trellis serverless data management framework for variant calling of VA MVP whole-genome sequencing data.

AnnotationHive

AnnotationHive: A Cloud-based Annotation Engine

Swarm

Swarm: A Federated Cloud Framework for Large-scale Variant Analysis: https://doi.org/10.1371/journal.pcbi.1008977

encode_utils

Tools that are useful to any ENCODE submitting group.

googva

pulsar_lims

A LIMS for ENCODE submitting labs.

Scoring

gene_coverage

Calculates gene-level breadth-of-coverage at several depth-of-coverage thresholds.

env-modules

Environment modules

healthkit-exporter

Exports HealthKit data to flat files.

trellis-mvp-analysis

Jupyter notebooks for analyzing $5 GATK (GATK-MVP) invoked in the Trellis Data management system using Neo4J graph DB and Google Cloud

Jupyter Notebook

hmp2lims

Project 3: Prediabetes Type 2 diabetes mellitus (T2D) is a significant health problem facing our nation. Close to 20 million individuals in the United States have T2D, and 79 million aged 20 years or older are clinically pre-diabetic, with a 5-year conversion rate of 10% to 23% from prediabetes to T2D. In a collaborative effort to systematically understand diabetes and its etiology, the team is comprised of leading experts in research on both the human host as well as the microbiome, as properties of both are likely relevant in T2D development. For a better elucidation of mechanisms of onset and progression of T2D disease, the group is performing a detailed analysis of the biological processes that occur in the microbiome and human host by longitudinal profiling of patients at risk for T2D. Both microbiome and host profiles are being analyzed by state-of-the-art omics platforms, and these large-scale and diverse data sets will be integrated to determine the dynamic pathways that change during the onset and progression of T2D, especially during viral infections and other stresses. This longitudinal study is expected to reveal changes in the microbiome and host at an unprecedented level of detail, and identify molecules and pathways that play important roles in diabetes onset and progression.

DEFUNCT-env-modules

trjread-source

Source files for DNAnexus applets

gcp_utils

Scripts to manage access to the Google Cloud Platform

vizzle

A collection of tools to create plots and figures. Initially targeting QC metrics, but can be expanded to other types of data.

trellis-mvp-api

Specifications for creating APIs to interact with the Trellis Neo4j metadata store

pubble

effective-practices

Documentation of effective practices used in the Stanford Center for Genomics& Personalized Medicine.

StanfordDeepMedicine

qualia

Scripts for preparation and processing of exome data from Claritas

trellis-docs

Read the Docs source for Trellis data management framework

trellis-mvp-terraform

Trellis MVP Terraform configurations

coverage_stats

Post-processing of GATK DepthOfCoverage output to gather genelist-specific stats and other metrics.

trellis-mvp-gatk

GATK with five-dollar-genome-analysis-pipeline on GCP for MVP