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."

Cancer biomarkers: Written in blood

http://www.nature.com/news/cancer-biomarkers-written-in-blood-1.15624

DNA circulating in the bloodstream could guide cancer treatment — if researchers can work out how best to use it.

But researchers have found ways to get a richer view of a patient's cancer, and even track it over time. When cancer cells rupture and die, they release their contents, including circulating tumour DNA (ctDNA): genome fragments that float freely through the bloodstream. Debris from normal cells is normally mopped up and destroyed by 'cleaning cells' such as macrophages, but tumours are so large and their cells multiply so quickly that the cleaners cannot cope completely.

The first practical use of circulating DNA came in another field. Dennis Lo, a chemical pathologist now at the Chinese University of Hong Kong, reasoned that if tumours could flood the blood with DNA, surely fetuses could, too. In 1997, he successfully showed that pregnant women carrying male babies had fetal Y chromosomes in their blood6. That discovery allowed doctors to check a baby's sex early in gestation without disturbing the fetus, and ultimately to screen for developmental disorders such as Down's syndrome without resorting to invasive testing. It has revolutionized the field of prenatal diagnostics (see Nature 507, 19; 2014).

Despite its promise, ctDNA is not yet ready for a starring role in the clinic. For one thing, the most sensitive techniques for detecting it, such as BEAMing, rely on some knowledge of which mutations to look for. This knowledge can be provided by taking a biopsy, sequencing its mutations, designing patient-specific molecular probes that target them, and using those probes to analyse later blood samples — a laborious approach that must be repeated for each patient. The alternative is to use exome sequencing, as Rosenfeld's team did. This requires no previous knowledge about the cancer, but it is prohibitively expensive to sequence and analyse every sample at the depth required to detect rare mutant fragments.

Experts question Google’s new ‘moonshot’ project: mapping human genome biomarkers

Canadian experts have concerns about a report that Google Inc. is planning to create a map of the biomarkers in the “healthy” human genome.

Researchers have told The Globe and Mail they would welcome the search giant to this area of study, which they say is underfunded, but they questioned how useful Google’s project would be based on the relatively small number of people it would involve (just 175 initially).

Frequentists vs Bayesian

http://oikosjournal.wordpress.com/2011/10/11/frequentist-vs-bayesian-statistics-resources-to-help-you-choose/

Most ecologists use the frequentist approach. This approach focuses on P(D|H), the probability of the data, given the hypothesis. That is, this approach treats data as random (if you repeated the study, the data might come out differently), and hypotheses as fixed (the hypothesis is either true or false, and so has a probability of either 1 or 0, you just don’t know for sure which it is). This approach is called frequentist because it’s concerned with the frequency with which one expects to observe the data, given some hypothesis about the world. The P values you see in the “Results” sections of most empirical ecology papers are values of P(D|H), where H is usually some “null” hypothesis.

Bayesian statistical approaches are increasingly common in ecology. Bayesian statistics focuses on P(H|D), the probability of the hypothesis, given the data. That is, this approach treats the data as fixed (these are the only data you have) and hypotheses as random (the hypothesis might be true or false, with some probability between 0 and 1). This approach is called Bayesian because you need to use Bayes’ Theorem to calculate P(H|D).

I guess I lean more towards Bayesian statistics! There's probably life on Mars =)

Biological insights from 108 schizophrenia-associated genetic loci

Biological insights from 108 schizophrenia-associated genetic loci 
Abstract: Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.

Subject terms: Genome-wide association studies
http://www.nature.com/nature/journal/v511/n7510/full/nature13595.html

Genomic inflation factors under polygenic inheritance 
Abstract:  Population structure, including population stratification and cryptic relatedness, can cause spurious associations in genome-wide association studies (GWAS). Usually, the scaled median or mean test statistic for association calculated from multiple single-nucleotide-polymorphisms across the genome is used to assess such effects, and ‘genomic control' can be applied subsequently to adjust test statistics at individual loci by a genomic inflation factor. Published GWAS have clearly shown that there are many loci underlying genetic variation for a wide range of complex diseases and traits, implying that a substantial proportion of the genome should show inflation of the test statistic. Here, we show by theory, simulation and analysis of data that in the absence of population structure and other technical artefacts, but in the presence of polygenic inheritance, substantial genomic inflation is expected. Its magnitude depends on sample size, heritability, linkage disequilibrium structure and the number of causal variants. Our predictions are consistent with empirical observations on height in independent samples of ~4000 and ~133 000 individuals.

Keywords: genome-wide association study, genomic inflation factor, polygenic inheritance
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3137506/

In population genetics, linkage disequilibrium is the non-random association of alleles at two or more loci, that descend from single, ancestral chromosomes
linkage equilibrium D = 0, is when PA * PB = PAB (ie. A is found and B is found)

http://en.wikipedia.org/wiki/Linkage_disequilibrium

Linkage disequilibrium makes tightly linked 
variants strongly correlated producing cost 
savings for association studies



http://www.sph.umich.edu/csg/abecasis/class/666.03.pdf

 Population stratification is the presence of a systematic difference in allele frequencies between subpopulations in a population possibly due to different ancestry, especially in the context of association studies.

The two most widely used approaches to this problem include genomic control, which is a relatively nonparametric method for controlling the inflation of test statistics,[2] and structured association methods,[3] which use genetic information to estimate and control for population structure.

Genomic Control works by using markers that are not linked with the trait in question to correct for any inflation of the statistic caused by population stratification
http://en.wikipedia.org/wiki/Population_stratification 

The Hardy–Weinberg principle states that allele and genotype frequencies in a population will remain constant from generation to generation in the absence of other evolutionary influences.

STAP Papers Retracted

http://www.genomeweb.com//node/1411071?utm_source=SilverpopMailing&utm_medium=email&utm_campaign=Daily%20Scan%20Blog:%20STAP%20Papers%20Retracted,%20This%20Week%20in%20Cell,%20Women%20in%20Elite%20Labs,%20more%20-%2007/02/2014%2001:05:00%20PM

The pair of Nature papers published in January that purported to show how to generate embryonic stem cell-like cells using stimuli like low pH has been retracted by the journal.

The retraction notice from Riken's Haruko Obokata and her colleagues says that "[s]everal critical errors have been found in our Article and Letter" and that a subsequent investigation of those errors by Riken found evidence of research misconduct.