VariantSpark - a Spark library for genomics

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VariantSpark a customer Apache Spark library for genomic data. Customer wide random forest machine learning algorithm, designed for workloads with millions of features.

Ciencias

VariantSpark: a library for Genomics
Transformational Bioinformatics | Denis C. Bauer | @allPowerde
Lynn Langit

Natalie Twine
Transformational Bioinformatics Team
Transformational Bioinformatics | Denis C. Bauer | @allPowerde
Denis Bauer Oscar Luo Rob Dunne Piotr SzulAidan O’BrienLaurence Wilson
Adrian White
Mia Champion
Gaetan Burgio
Collaborators
David Levy
News
Software
Dan Andrews
Kaitao Lai
Kaylene Simpson
Iva Nikolic
Ian Blair
Kelly Williams

BMC Genomics 2015, 16:1052 PMID: 26651996 (IF=4)
Cited
4
VariantSpark | Denis C. Bauer @allPowerde

Unsupervised ML : K-Means
www.cloudaccess.eu
1000 x 40 Million variants
Matrix *
k-means
Predict super
population
4
14 ethnic groups and
s u p e r
populations
VariantSpark | Denis C. Bauer @allPowerde
* VariantSpark can also process phase 3 data: 3000 individuals and 80 million variants

Comparing K-Means Implementations
0
1000
2000
Python
R
H
adoop
Adam
AD
M
IXTU
R
E
VariantSpark
method
timeinseconds
task
binary−conversion
clustering
pre−processing
103 75 29 28 18 4 min
VariantSpark | Denis C. Bauer @allPowerde

Supervised ML: Wide Random Forests
Transformational Bioinformatics | Denis C. Bauer | @allPowerde

Transformational Bioinformatics | Denis C. Bauer | @allPowerde
Genomic Research Workflow
https://www.projectmine.com/about/
Focus

Performance – Faster and More Accurate
VariantSpark is the only method to scale to 100% of the genome
Transformational Bioinformatics | Denis C. Bauer | @allPowerde

Scaling to 50 M variables and 10 K samples
Transformational Bioinformatics | Denis C. Bauer | @allPowerde
100K trees: 5 – 50h
AWS: ~$215.50
100K trees: 200 – 2000h
AWS: ~ $ 8620.00
• Yarn Cluster (12 workers)
• 16 x Intel Xeon E5-2660@2.20GHz CPU
• 128 GB of RAM
• Spark 1.6.1 on YARN
• 128 executors
• 6GB / executor (0.75TB)
• Synthetic dataset (mtry = 0.25)
Whole Genome
Range
GWAS Range

Databricks &
VariantSpark
via a Jupyter notebook

Solving Important Questions…
Cancer genomics?

• Quickly access a managed Spark cluster - AWS EC2 / spot instances
• Link to your data and perform whole genome analysis in real-time
VariantSpark & Databricks Notebooks
Transformational Bioinformatics | Denis C. Bauer | @allPowerde
Jupyter Notebook
Transformational Bioinformatics | Denis C. Bauer | @allPowerde

Try it out: VariantSpark Notebook
https://databricks.com/blog/2017/07/26/breaking-the-
curse-of-dimensionality-in-genomics-using-wide-
random-forests.html

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Cotton crops are vulnerable to a variety of sucking pests, which can severely impact plant health, yield, and fiber quality. These pests primarily feed on plant sap, extracting nutrients directly from the plant's vascular system. Here's a breakdown of some of the most significant sucking pests in cotton cultivation: Aphids: Cotton aphids or melon aphids can cause direct damage by sucking sap and indirect damage by secreting honeydew, which encourages sooty mold growth. This can interfere with photosynthesis and weaken the plant. Aphids can also transmit viral diseases. Whiteflies: Two species, the silverleaf whitefly and the bandedwinged whitefly, are particularly troublesome. They not only suck sap from the underside of leaves, causing yellowing and leaf drop, but their honeydew excretion promotes sooty mold and they can transmit several plant viruses. Thrips: While thrips can chew on plants, their primary damage to cotton is through sucking. They attack the cotton plant during its seedling stage, which can stunt growth and reduce vigor. Thrips are also capable of transmitting the Cotton Bud disease. Spider Mites: These are not insects but arachnids. Spider mites, such as the two-spotted spider mite, suck cell contents from the leaves, leading to speckled discoloration and potentially significant leaf loss if infestations are severe. Leafhoppers: Including various species, leafhoppers can cause direct damage through feeding, which results in leaf curling and stunted growth. They can also be vectors for plant diseases. Mealybugs: These pests are less common but can be problematic, especially in clustered planting conditions. They suck sap and secrete honeydew, which leads to sooty mold. Mealybugs can also spread viruses. Stink Bugs: Although primarily known for their chewing mouthparts, certain stink bugs can cause damage similar to sucking pests by injecting saliva into the plant and sucking out nutrients, leading to boll damage and stained lint. Management Strategies: Cultural Controls: This includes practices such as crop rotation, using resistant varieties, and managing planting and harvesting times to avoid peak pest populations. Biological Controls: Beneficial insects like lady beetles, lacewings, and predatory mites can naturally control sucking pest populations. Parasitic wasps also play a role in controlling aphid and whitefly populations. Chemical Controls: Insecticides may be used but should be chosen carefully to minimize resistance development and preserve beneficial insects. Systemic insecticides can be particularly effective against sucking pests. Integrated Pest Management (IPM): Combining multiple control strategies based on monitoring and thresholds to apply the most effective and environmentally sensitive approach. Effective management of sucking pests in cotton requires a thorough understanding of the pest species present, their life cycles, and the ecological balance of the field environment.

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Sumit Kumar yadav

CELL -Structural and Functional unit of life.pdf

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Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)

Joonhun Lee

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VariantSpark - a Spark library for genomics

1. VariantSpark: a library for Genomics Transformational Bioinformatics | Denis C. Bauer | @allPowerde Lynn Langit

2. “Genomical” Big Data

3. Natalie Twine Transformational Bioinformatics Team Transformational Bioinformatics | Denis C. Bauer | @allPowerde Denis Bauer Oscar Luo Rob Dunne Piotr SzulAidan O’BrienLaurence Wilson Adrian White Mia Champion Gaetan Burgio Collaborators David Levy News Software Dan Andrews Kaitao Lai Kaylene Simpson Iva Nikolic Ian Blair Kelly Williams

4. BMC Genomics 2015, 16:1052 PMID: 26651996 (IF=4) Cited 4 VariantSpark | Denis C. Bauer @allPowerde

5. Unsupervised ML : K-Means www.cloudaccess.eu 1000 x 40 Million variants Matrix * k-means Predict super population 4 14 ethnic groups and s u p e r populations VariantSpark | Denis C. Bauer @allPowerde * VariantSpark can also process phase 3 data: 3000 individuals and 80 million variants

6. Comparing K-Means Implementations 0 1000 2000 Python R H adoop Adam AD M IXTU R E VariantSpark method timeinseconds task binary−conversion clustering pre−processing 103 75 29 28 18 4 min VariantSpark | Denis C. Bauer @allPowerde

7. Supervised ML: Wide Random Forests Transformational Bioinformatics | Denis C. Bauer | @allPowerde

8. Transformational Bioinformatics | Denis C. Bauer | @allPowerde Genomic Research Workflow https://www.projectmine.com/about/ Focus

9. Performance – Faster and More Accurate VariantSpark is the only method to scale to 100% of the genome Transformational Bioinformatics | Denis C. Bauer | @allPowerde

10. Scaling to 50 M variables and 10 K samples Transformational Bioinformatics | Denis C. Bauer | @allPowerde 100K trees: 5 – 50h AWS: ~$215.50 100K trees: 200 – 2000h AWS: ~ $ 8620.00 • Yarn Cluster (12 workers) • 16 x Intel Xeon E5-2660@2.20GHz CPU • 128 GB of RAM • Spark 1.6.1 on YARN • 128 executors • 6GB / executor (0.75TB) • Synthetic dataset (mtry = 0.25) Whole Genome Range GWAS Range

11.

12. Databricks & VariantSpark via a Jupyter notebook

13. Solving Important Questions… Cancer genomics?

14. DEMO: Who is a Hipster?

15. • Quickly access a managed Spark cluster - AWS EC2 / spot instances • Link to your data and perform whole genome analysis in real-time VariantSpark & Databricks Notebooks Transformational Bioinformatics | Denis C. Bauer | @allPowerde Jupyter Notebook Transformational Bioinformatics | Denis C. Bauer | @allPowerde

16. Joint-loci association test Hipster-Index = ((2 + GT[B6]) * (1.5 + GT[R1])) + ((0.5 + GT[C2]) * (1 + GT[B2])) Label = 1 if Hipster-Index>10 Genomic profile Label Samples(n=2500) Transformational Bioinformatics | Denis C. Bauer | @allPowerde

17. Try it out: VariantSpark Notebook https://databricks.com/blog/2017/07/26/breaking-the- curse-of-dimensionality-in-genomics-using-wide- random-forests.html

18. VariantSpark: a library for Genomics Transformational Bioinformatics | Denis C. Bauer | @allPowerde Lynn Langit

Notas del editor

http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002195
http://www.cloudaccess.eu/blog/wp-content/uploads/2014/06/genetic_roots.png
Chromosome 22; VM on Microsoft Azure with A7 Linux instance and 8 cores, 56GB memory running Ubuntu.
https://academics.cloud.databricks.com/#notebook/170398/command/170419
https://databricks.com/blog/2017/07/26/breaking-the-curse-of-dimensionality-in-genomics-using-wide-random-forests.html

VariantSpark - a Spark library for genomics

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Notas del editor