![# Compute the Ti/Tv ratio for BRCA1.
SELECT
transitions,
transversions,
transitions/transversions AS titv
FROM (
SELECT
SUM(IF(mutation IN ('A->G',
'G->A',
'C->T',
'T->C'),
INTEGER(num_snps),
INTEGER(0))) AS transitions,
SUM(IF(mutation IN ('A->C',
'C->A',
'G->T',
'T->G',
'A->T',
'T->A',
'C->G',
'G->C'),
INTEGER(num_snps),
INTEGER(0))) AS transversions,
FROM (
SELECT
CONCAT(reference_bases,
CONCAT(STRING('->'),
alternate_bases)) AS mutation,
COUNT(alternate_bases) AS num_snps,
FROM
[google.com:biggene:1000genomes.variants1kG]
WHERE
contig = '17'
AND position BETWEEN 41196312
AND 41277500
AND vt = 'SNP'
GROUP BY
mutation
ORDER BY
mutation));
Google BigQuery with plot
result <- query_exec(project = "google.com:biggene", dataset =
"1000genomes",
query = sql, billing = billing_project)
Ti/Tv ratio in BRCA1](https://image.slidesharecdn.com/genomicsinbigdata-140508021143-phpapp01/85/Genomics-and-BigData-case-study-57-320.jpg)
![# Count the variation for each sample including phenotypic traits
SELECT
samples.genotype.sample_id AS sample_id,
gender,
population,
super_population,
COUNT(samples.genotype.sample_id) AS num_variants_for_sample,
SUM(IF(samples.af >= 0.05,
INTEGER(1),
INTEGER(0))) AS common_variant,
SUM(IF(samples.af < 0.05
AND samples.af > 0.005,
INTEGER(1),
INTEGER(0))) AS middle_variant,
SUM(IF(samples.af <= 0.005
AND samples.af > 0.001,
INTEGER(1),
INTEGER(0))) AS rare_variant,
SUM(IF(samples.af <= 0.001,
INTEGER(1),
INTEGER(0))) AS very_rare_variant,
FROM
FLATTEN([google.com:biggene:1000genomes.variants1kG],
genotype) AS samples
JOIN
[google.com:biggene:1000genomes.sample_info] p
ON
samples.genotype.sample_id = p.sample
WHERE
samples.vt = 'SNP'
AND (samples.genotype.first_allele > 0
OR samples.genotype.second_allele > 0)
GROUP BY
sample_id,
gender,
population,
super_population
ORDER BY
sample_id;
Google BigQuery with R
ggplot(result, aes(x = population, y = common_variant, fill =
super_population)) +
geom_boxplot() + ylab("Count of common variants per sample") +
ggtitle("Common Variants (Minimum Allelic Frequency 5%)")
Variant type](https://image.slidesharecdn.com/genomicsinbigdata-140508021143-phpapp01/85/Genomics-and-BigData-case-study-58-320.jpg)
Genomics and BigData - case study
- 1. d奪巽達叩谷=叩奪=_叩=a~鱈~== 觜一危=蟯=豌=一危 Permissions: you are free to blog or live-blog about this presentation as long as you attribute the work to its authors Chang Bum Hong
- 2. る 伎手鍵.. Genomics 企殊磯 Cloud Computing Cloud in Genomics 襦襦 危ｴ覲企 Big Data 覿 HPC Cloud
- 3. 1,588覈 旧 螳企 54%螳 蟆 覿 覲願 矩り Nature, 2010
- 4. 蠍一郁規 襭 覿殊 螳 譴蟆企, 5-10 螳 襷豢れ 覲危ｸ 蟆, 讌襷 貉危螻 語 覿譟煙 蟇碁朱 Nature, 2010
- 6. Data Size
- 10. Computing resources Fusaro VA, Patil P, Gafni E, Wall DP, Tonellato PJ (2011) Biomedical Cloud Computing With Amazon Web Services. PLoS Comput Biol 7(8): e1002147
- 11. Linux skill Nature Biotech (2006)
- 12. To the Clinic - License, HIPPA
- 13. To the Clinic - License, HIPPA
- 14. To the Clinic - License, HIPPA
- 15. Cloud Computing and HPC (Hight Performance Computing)
- 16. 貉危一 る誤 蠍壱, 讌// 螳, 螻旧 磯Μ螳 螻 螳 企殊磯
- 17. る 伎手鍵 企殊磯
- 18. る 伎手鍵 企殊磯 Journal of Biomedical Informatics (2013)
- 19. 伎 螳訖襷 , れ 觜る Amazon Web Services 觜 覈襦
- 20. 伎 螳訖襷 , れ 觜る Google 觜 覈襦
- 21. 伎 螳襦 覿 視 蟆 OS襯 螳ロ 襷豢 OS 螳ロ
- 22. (reproducibility) 覲 覿 蟆 蟲豢 覿 蟆 + 一危 + ろ襴渚 覿 蟆 + 一危 + ろ襴渚呉 覦襦 朱れ るジ り骸 螻旧
- 23. 伎 螳襦 覿 視 蟆 伎譴 覯 ろ 豢螳 ろ 伎 企語 螻旧
- 24. 伎 螳襦 覿 視 蟆 覯襯 襷 煙 螳ロ
- 25. 伎 螳襦 覿 視 蟆 るジ ろ襴讌 觜 Object Storage http://whatis.techtarget.com/reference/Object-storage-Fast-Guide
- 26. 伎 螳襦 覿 視 蟆 るジ ろ襴讌 觜 Object Storage
- 27. 覈蟆 覦襦 Cloud Software -IaaS 蟲豢 螳ロ ろ 企殊磯 OpeStack, Eucalyptus, OpenNebula, CloudStack
- 29. HPC Clustering
- 31. Cloud in Genomics Genomics 蟲豌伎 cloud 襦 觜 蟲覿 觜る 觜 伎 KT IaaS Amazon WebServices PaaS, SaaS 觜ろれ 蠏 讌 public dataset 牛 螳 讌 ucloud biz Google Cloud 貉危螻 ろ襴讌 讌 ucloud biz NeCTAR Research Cloud OpenStack 蠍磯 郁規襯 Private Cloud ucloud biz SaaS DNANexus NGS 一危 覿 危殊 螻 g-Analysis SevenBridge Genomics NGS 一危 覿 危殊 螻 g-Analysis GotCloud NGS 一危 覿 危殊 螻 g-Analysis Globus Genomics Galaxy襯 AWS 蠍磯朱 螻 g-Galaxy GenomeSpace Storage 蠍磯 bioinformatics 觜 螻 g-Storage PaaS CloudMan ろ襴讌 蠍磯 Bioinformatics 讌 SeqWare 豌 覿 螳ロ 蠍磯 螻 StarCluster AWS 蠍磯 HPC Cluster 貉危 蟆 螻 CycleComputing AWS 蠍磯 HPC Cluster 貉危 蟆 螻 Google Genomics, BigQeury NGS 一危 覿 API 螻 g-Insight
- 32. 襦襦 危ｴ覲企 Big Data 覿 HPC Cloud
- 34. HPC Cloud - Sequencers http://seqware.github.io/docs/ LIMS Object Storage High Speed File Transfer IaaS HPC Private Cloud OpenStack Job Schedule Bioinformatics Linux Bioinformatics Bioinformatics Hadoop and Database
- 35. LIMS for NGS Clarity LIMS from GenoLogics Galaxy LIMS for NGS Bioinformatics (2013)
- 36. LIMS for NGS Galaxy LIMS https://bitbucket.org/jelle/galaxy-central-tron-lims/
- 37. Data Upload - online
- 38. Data Upload - offline Import/Export
- 39. HPC Cloud - Storage http://seqware.github.io/docs/ LIMS Object Storage High Speed File Transfer IaaS HPC Private Cloud OpenStack Job Schedule Bioinformatics Linux Bioinformatics Bioinformatics Hadoop and Database
- 40. http://www.genomespace.org/ GenomeSpace is a cloud-based interoperability framework to support integrative genomics analysis through an easy-to-use Web interface.
- 41. HPC Cloud - HPC http://seqware.github.io/docs/ LIMS Object Storage High Speed File Transfer IaaS HPC Private Cloud OpenStack Job Schedule Bioinformatics Linux Bioinformatics Bioinformatics Hadoop and Database
- 42. Before Cloud Computing 蟲襷 豌 -> 蟆->覯 覲>伎れ-> 語 れ -> 豕譬 ろ ->蟲=I=蟲=一危
- 43. NeCTAR
- 45. HPC Cloud - Pipeline http://seqware.github.io/docs/ LIMS Object Storage High Speed File Transfer IaaS HPC Private Cloud OpenStack Job Schedule Bioinformatics Linux Bioinformatics Bioinformatics Hadoop and Database
- 50. HPC Cloud - Web Service http://seqware.github.io/docs/ LIMS Object Storage High Speed File Transfer IaaS HPC Private Cloud OpenStack Job Schedule Bioinformatics Linux Bioinformatics Bioinformatics Hadoop and Database
- 52. Google Genomics API http://googleresearch.blogspot.co.uk/2014/02/google-joins-global-alliance-for.html Interoperability: One API, Many Apps
- 53. Google Genomics Examples API襯 伎 轟 轟 read 覲 豢豢
- 54. Google Genomics Examples API JavaScript襯 伎 Genome Browser
- 55. HPC Cloud - Query Engine http://seqware.github.io/docs/ LIMS Object Storage High Speed File Transfer IaaS HPC Private Cloud OpenStack Job Schedule Bioinformatics Linux Bioinformatics Bioinformatics Hadoop and Database
- 56. Google BigQuery
- 57. # Compute the Ti/Tv ratio for BRCA1. SELECT transitions, transversions, transitions/transversions AS titv FROM ( SELECT SUM(IF(mutation IN ('A->G', 'G->A', 'C->T', 'T->C'), INTEGER(num_snps), INTEGER(0))) AS transitions, SUM(IF(mutation IN ('A->C', 'C->A', 'G->T', 'T->G', 'A->T', 'T->A', 'C->G', 'G->C'), INTEGER(num_snps), INTEGER(0))) AS transversions, FROM ( SELECT CONCAT(reference_bases, CONCAT(STRING('->'), alternate_bases)) AS mutation, COUNT(alternate_bases) AS num_snps, FROM [google.com:biggene:1000genomes.variants1kG] WHERE contig = '17' AND position BETWEEN 41196312 AND 41277500 AND vt = 'SNP' GROUP BY mutation ORDER BY mutation)); Google BigQuery with plot result <- query_exec(project = "google.com:biggene", dataset = "1000genomes", query = sql, billing = billing_project) Ti/Tv ratio in BRCA1
- 58. # Count the variation for each sample including phenotypic traits SELECT samples.genotype.sample_id AS sample_id, gender, population, super_population, COUNT(samples.genotype.sample_id) AS num_variants_for_sample, SUM(IF(samples.af >= 0.05, INTEGER(1), INTEGER(0))) AS common_variant, SUM(IF(samples.af < 0.05 AND samples.af > 0.005, INTEGER(1), INTEGER(0))) AS middle_variant, SUM(IF(samples.af <= 0.005 AND samples.af > 0.001, INTEGER(1), INTEGER(0))) AS rare_variant, SUM(IF(samples.af <= 0.001, INTEGER(1), INTEGER(0))) AS very_rare_variant, FROM FLATTEN([google.com:biggene:1000genomes.variants1kG], genotype) AS samples JOIN [google.com:biggene:1000genomes.sample_info] p ON samples.genotype.sample_id = p.sample WHERE samples.vt = 'SNP' AND (samples.genotype.first_allele > 0 OR samples.genotype.second_allele > 0) GROUP BY sample_id, gender, population, super_population ORDER BY sample_id; Google BigQuery with R ggplot(result, aes(x = population, y = common_variant, fill = super_population)) + geom_boxplot() + ylab("Count of common variants per sample") + ggtitle("Common Variants (Minimum Allelic Frequency 5%)") Variant type
- 59. Google BigQuery with RStudio Markdown/Knit HTML
- 60. Publish your R code with git and RPubs https://github.com/ http://www.rpubs.com/
- 61. Data storage: $0.026 (per GB/month) Data query: $0.005 / GB 1000 genomes data Data storage: $0.026 (per GB/month) * 5,500 GB = $143 / month = 15,700/ Data query (allele frequency query): $0.005 / GB * 647 GB = $3.3 = 3,600 Cost 30豐 1000genomes 覈 allele frequency襯 蟲.
- 62. Conclusion 企 讌蠍 伎螳蟾讌 譬覓殊碁 . 襷 螻殊 れ伎. 襷 螻殊襯 伎.