8 Realities of the Sequencing GWAS

http://massgenomics.org/2014/03/gwas-sequencing-realities.html

For several years, the genome-wide association study (GWAS) has served as the flagship discovery tool for genetic research, especially in the arena of common diseases. The wide availability and low cost of high-density SNP arrays made it possible to genotype 500,000 or so informative SNPs in thousands of samples. These studies spurred development of tools and pipelines for managing large-scale GWAS, and thus far they’ve revealed hundreds of new genetic associations.

As we all know, the cost of DNA sequencing has plummeted. Now it’s possible to do targeted, exome, or even whole-genome sequencing in cohorts large enough to power GWAS analyses. While we can leverage many of the same tools and approaches developed for SNP array-based GWAS, the sequencing data comes with some very important differences.

The Leek group guide to genomics papers

https://github.com/jtleek/genomicspapers/

Variant Discovery, Annotation & Filtering With Samtools & the GATK

http://petridishtalk.com/2013/02/01/variant-discovery-annotation-filtering-with-samtools-the-gatk/

Life sciences institute public talks

http://lsi.ubc.ca/news-events/events/public-talks/

Git pretty log

git log --graph --decorate --oneline --all

git diff <c3b37f0>

MySQL Spatial Queries

http://howto-use-mysql-spatial-ext.blogspot.ca/

org.hibernate.dialect.MySQLDialect

MySQL5Dialect, MySQLInnoDBDialect, MySQLMyISAMDialect
 https://docs.jboss.org/hibernate/orm/3.6/javadocs/org/hibernate/dialect/MySQLDialect.html

drop table test;

create table test (
    id INT NOT NULL PRIMARY KEY,
    location LINESTRING NOT NULL,
    SPATIAL KEY sx_location (location)
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

set @a = linestring(point(1,1),point(5,1));
set @b = linestring(point(1,1),point(10,1));

insert into test values(1, @a);
insert into test values(2, @b);

select * from test where MBRtouches(location, linestring(point(1,1),point(5,1)) );
select * from test where MBRtouches(location, linestring(point(2,1),point(3,1)) );
select * from test where MBRtouches(location, linestring(point(1,1),point(2,1)) );
select * from test where MBRtouches(location, linestring(point(1,1),point(7,1)) );
select * from test where MBRtouches(location, linestring(point(-1,1),point(0,1)) );
select * from test where MBRtouches(location, linestring(point(0,1),point(1,1)) );
select * from test where MBRtouches(location, linestring(point(7,1),point(12,1)) );
select * from test where MBRtouches(location, linestring(point(12,1),point(20,1)) );

select MBRcontains(@b,@a);
select MBRcontains(@a,@b);
select MBRoverlaps(@a,@b);
select MBRoverlaps(@b,@a);
select MBRtouches(@a,@b);

Hibernate SQL statement debug

log4j.logger.org.hibernate.SQL=TRACE
log4j.logger.org.hibernate.type.descriptor.sql.BasicBinder=TRACE

Microarray vs RNA-seq, Normalization

John Storey provides his take on the importance of new statistical methods for high-throughput sequencing.

http://www.nature.com/nbt/journal/v29/n4/full/nbt.1831.html

Honing our reading skills

http://www.nature.com/nbt/journal/v32/n9/full/nbt.3021.html?WT.ec_id=NBT-201409

Nature Biotechnology Contents: Volume 32 pp 700 - 960
http://mabsj2.blogspot.ca/2014/09/nature-biotechnology-contents-volume-32.html

The devil in the details of RNA-seq
http://www.nature.com/nbt/journal/v32/n9/full/nbt.3015.html#affil-auth
RNA-seq is clearly superior to microarrays for its ability for de novo discovery and detection of genes, especially those with low expression levels. The detection of alternative splicing patterns is possible, but attention needs to be paid to the underlying gene annotation, and parameters such as mapping and error rates become more important than sequencing depth.

Detecting and correcting systematic variation in large-scale RNA sequencing data
Sheng Li,    Paweł P Łabaj,    Paul Zumbo,    Peter Sykacek,    Wei Shi,    Leming Shi,    John Phan,    Po-Yen Wu,    May Wang,    Charles Wang,    Danielle Thierry-Mieg,    Jean Thierry-Mieg,    David P Kreil & Christopher E Mason
AffiliationsContributionsCorresponding authors
Nature Biotechnology 32, 888–895 (2014) doi:10.1038/nbt.3000

Abstract• Introduction• Results• Discussion• Methods• Accession codes• References• Acknowledgments• Author information• Supplementary information
High-throughput RNA sequencing (RNA-seq) enables comprehensive scans of entire transcriptomes, but best practices for analyzing RNA-seq data have not been fully defined, particularly for data collected with multiple sequencing platforms or at multiple sites. Here we used standardized RNA samples with built-in controls to examine sources of error in large-scale RNA-seq studies and their impact on the detection of differentially expressed genes (DEGs). Analysis of variations in guanine-cytosine content, gene coverage, sequencing error rate and insert size allowed identification of decreased reproducibility across sites. Moreover, commonly used methods for normalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality. Normalization methods that combine data from genes across sites are strongly recommended to identify and remove site-specific effects and can substantially improve RNA-seq studies.

http://www.nature.com/nbt/journal/v32/n9/full/nbt.3000.html?WT.ec_id=NBT-201409

http://www.nature.com/nbt/journal/v32/n9/full/nbt.2931.html?WT.ec_id=NBT-201409

http://www.nature.com/nbt/journal/v32/n9/full/nbt.2957.html?WT.ec_id=NBT-201409

http://www.nature.com/nbt/journal/v32/n9/full/nbt.2972.html?WT.ec_id=NBT-201409

http://www.nature.com/nbt/journal/v32/n9/full/nbt.3001.html?WT.ec_id=NBT-201409

Eclipse hotkeys

Shift hover over function shows code
Alt-left, Alt-right previous view
F3
Ctrl+F3 outline
Ctrl+hover open declaration, implementation

RESTful web api for getting gene information

NCBI's E-Utils

BRCA1
http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=gene&id=672

BioMart
http://central.biomart.org/martwizard/#!/Search_by_database_name?mart=Hugo+Gene+Nomenclature+(HGNC)+(EBI%2C+UK)&step=1&datasets=hgnc
<!DOCTYPE Query><Query client="true" processor="TSV" limit="-1" header="1"><Dataset name="hgnc" config="hgnc_config_1"><Filter name="gd_status" value="Approved" filter_list=""/><Filter name="gd_app_sym" value="BRCA1" filter_list=""/><Attribute name="gd_aliases"/></Dataset></Query>

HGNC
http://www.genenames.org/cgi-bin/hgnc_downloads.cgi?title=HGNC+output+data&hgnc_dbtag=on&col=gd_app_sym&col=gd_aliases&status=Approved&status=Entry+Withdrawn&status_opt=2&where=&order_by=gd_app_sym_sort&format=text&limit=&submit=submit&.cgifields=&.cgifields=chr&.cgifields=status&.cgifields=hgnc_dbtag

One Codex Wants To Be The Google For Genomic Data

http://techcrunch.com/2014/08/15/one-codex-wants-to-be-the-google-for-genomic-data/

As hospitals and public health organizations switch to using genomic data for testing, searching through genomic data can still take some time. Y Combinator-backed startup, One Codex, wants to help researchers, clinicians and public health officials, who have sequenced more than 100,000 genomes and created petabytes of data, to search this data.

For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights

http://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html?_r=0
 
Yet far too much handcrafted work — what data scientists call “data wrangling,” “data munging” and “data janitor work” — is still required. Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.

ClearStory Data, a start-up in Palo Alto, Calif., makes software that recognizes many data sources, pulls them together and presents the results visually as charts, graphics or data-filled maps. Its goal is to reach a wider market of business users beyond data masters.

Trifacta makes a tool for data professionals. Its software employs machine-learning technology to find, present and suggest types of data that might be useful for a data scientist to see and explore, depending on the task at hand.

Human Longevity Project

http://www.genomeweb.com/blog/deeper-bench
 
With the launch of his new company, Human Longevity, this year, Venter aims to not only sequence tens of thousands of people, but also collect physiological data such as how much blood their heart can pump and brain size. So far, he tells Tech Review that his company has sequenced 500 people who are now beginning to undergo those additional tests.

"Google Translate started as a slow algorithm that took hours or days to run and was not very accurate. But Franz [Och] built a machine-learning version that could go out on the Web and find every article translated from German to English or vice versa, and learn from those," Venter says. "And then it was optimized, so it works in milliseconds."