Data and Python in Biology at PyData NYC 2015
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http://marianattestad.com How big data is transforming biology and how we are using Python to make sense of it all. Genome sequencing, big data in genomics, long-read sequencing, SplitThreader string graph library in Python, cancer research, and personal genomics! I am Maria Nattestad, a PhD student in computational biology at Cold Spring Harbor laboratory.
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Data and Python in Biology at PyData NYC 2015
- 1. How big data is transforming biology and how we are using Python to make sense of it all Maria Nattestad Computational biology PhD student PyData NYC 2015
- 2. Overview Genome sequencing Using Python to study cancer Personal genomics
- 3. Overview Genome sequencing Using Python to study cancer Personal genomics
- 4. Your genome 46 strings of A, T, C, and G for a total of about 6 billion characters male
- 5. Mutations in the genome can lead to cancer and other diseases Over 20,000 genes are scattered all over the genome. The genome is the instruction manual for creating a living thing. Some changes in the genome do nothing or encode normal variation like hair color, while others can cause disease.
- 6. Illumina = “Next-generation sequencing” Sanger = The original Human Genome Project publishes first draft
- 7. Big Data 3000 Rice Genomes Project
- 8. Sequencing by the numbers • Human genome is 6 billion letters [ATCG] • No technology exists that can read an entire chromosome from end to end • Illumina sequencing produces 100 letters of sequence • If the genome was random, this would be enough
- 9. The genome is not random ATCGATCAT?ATCGATCATA repeats Because of this the human genome STILL has gaps
- 10. Repeats make it harder to assemble the genome puzzle A B R CDR RR CR B R DR A R A B R C D If a repeat is longer than the reads
- 11. Long-read DNA Sequencing Pacific Biosciences Oxford Nanopore MinION >10X as expensive as next-generation (Illumina) sequencing >100X read length
- 12. Resolving repeats with long-read sequencing A R D CB R A B R R C D A R DCB R
- 13. Overview Genome sequencing Using Python to study cancer Personal genomics
- 14. How the human genome changes during cancer Normal human genome
- 15. How the human genome changes during cancer (Davidson et al, 2000) 80 chromosomes instead of 46 Cancer genome Cell line from a woman with metastatic breast cancer in 1971, tumor cells have been grown and studied in the lab ever since.
- 16. Split-read variant calling chromosome 1 chromosome 2
- 17. A simple gene fusion Gene1 Gene2 Gene1 Gene2
- 18. A complex gene fusion Gene1 Gene2 Gene1 Gene2
- 19. SplitThreader: A new Python graph library for representing rearranged genomes CHR 1 CHR 2 ATCGCCTA GTCCATAG 8 10 2 ATCG CCGA ATAGGTCC CHR 1 CHR 2 10 2 8
- 20. Class structure of SplitThreader Node Node NodeNode Edge Edge Edge Port Port Port Port Port Port Port Port Graph Edge Edge Edge Edge Once you enter a node, you must exit out the other side like a tunnel.
- 21. Biological insights from SplitThreader Depth first search or Breadth first search Gene fusion finding History of mutations
- 22. Using SplitThreader to find a gene fusion CYTH1 EIF3H CYTH1 EIF3H Goal is to find a path like this:
- 23. Too many copies of Her2 contributes to making cancer worse Sequencing Actual genome Her2 Too much Her2 Too much signal to divide Too many cell divisions Cancer grows
- 24. About 40 copies of Her2 gene scattered around the genome Her2 gene
- 25. Her2 Chr 17: 83 Mb 8 Mb
- 26. Her2 Her2
- 28. Her2 Chr 17 Chr 8 1. Healthy chromosome 17 2. Sequence copied into chromosome 8 3. Subsequence copied within chromosome 8 4. Complex variant and inverted duplication within chromosome 8 5. Subsequence copied within chromosome 8
- 29. SplitThreader is open source on Github ATCG CCGA ATAGGTCC CHR 1 CHR 2 10 2 8 https://github.com/marianattestad/splitthreader Visualization with D3.js is underway! Contributions are very welcome
- 30. Overview Genome sequencing Using Python to study cancer Personal genomics
- 31. Personal genomics SNP chip Sequencing • Illumina, SureGenomics • About $1,000 • Captures large and small mutations even if completely novel and unexpected • 23andMe • About $100 • Captures tiny mutations scientists already know to look for
- 32. Personal genomics debates • Should the government allow these companies to give people their genomic data? – How about interpreting the health risks? • Is sharing your own genome breaking your family’s privacy?
- 33. THANK YOU
Notas do Editor
- SKBR3 is from a pleural effusion (lung metastasis) of breast cancer isolated from a 43-year-old women in 1971. Spectral karyotyping (SKY)
- Woman with breast cancer in 1971, cells isolated from a metastasis in her lungs. Some cancer cells can grow in the lab, so scientists use them to learn about human biology without experimenting on patients.
- Normal cells have 2 copies.