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awk 1-liner+ MySQL

Updating SNP position on the chromosome, with liftOver et al

NCBI Build Vs UCSC Annotation Shenanigans! (Alt: How to tell which ncbi build corresponds to a UCSC hg##)

“GRCh37/hg19 and GRCh38 are genome builds rather than annotations, which describe where features are in a given genome build.

The actual sequences you'll get from NCBI/UCSC/Ensembl will be identical, but their annotations will be different

and (importantly) updated at different frequencies.

NCBI's annotation is the "refseq" dataset (the "refGene" track in UCSC), which is essentially a subset of the UCSC and Ensembl annotations.

•UCSC's annotations are kind of a mess.

You'll find genes with the same ID on multiple strand and multiple chromosomes, which makes them a bit useless.

•Ensembl's annotations typically contain more features than UCSC (so a bit more noise),

but they're otherwise much better put together (e.g., you'll never find a gene ID on different strand or different chromosomes) and their IDs are typically easier to map to other things (e.g., gene names, GO and pathway memberships).

Ensembl also updates its annotation fairly often and versions everything nicely, so it's quite convenient to report what version you used in a paper (reproducibility is always a good thing).

Given the choice, use the Ensembl annotation.

BTW, don't forget that the various sources can use different names for chromosomes (e.g., chr1 in UCSC is just 1 in Ensembl), so don't mix and match them.

#database #annotation #ensembl #ucsc #id #chr20

Phased data are ordered along one chromosome and so from these data you know the haplotype. Unphased data are simply the genotypes without regard to which one of the pair of chromosomes holds that allele.

If you are analysing the 1000G data taking each SNP as an independent data point, you most probably don't need phased data. If what you are studying are correlations between, say, pairs of SNPs, and can be influenced by recombination, like linkage disequilibrium or selective sweeps, then you need phased data.

PLINK to the rescue(?)

LiftOver is a necesary step to bring all genetical analysis to the same reference build. LiftOver can have three use cases:

(1) Convert genome position from one genome assembly to another genome assembly

In most scenarios, we have known genome positions in NCBI build 36 (UCSC hg 18) and hope to lift them over to NCBI build 37 (UCSC hg19).

(2) Convert dbSNP rs number from one build to another

(3) Convert both genome position and dbSNP rs number over different versions

It is likely to see such type of data in Merlin/PLINK format.

We will explain the work flow for the above three cases. In the rest of this article, our example is to lift over from lower/older build to newer/higher build, as it is the common practice.

Using different tools, liftOver can be easy. For example, UCSC liftOver tool is able to lift BED format file between builds. With our customized scripts, we can also lift rsNumber and Merlin/PLINK data files.

#cri5t1na #PLINK #liftover #ncbi_build_36 #ncbi_build

BUT WHY:

Reasons[0]: Use classes in project; probably appreciated

(not so) Subtly highlight your selfless spirit :p - Sharable

Safe documentation, functionality for future self-use: submit in repo final clean version, import anytime you re-need

Open Source as a chance to receive feedback for Optimization, tweaking 4 fine-tuning

+++

+++++ [..]

.......................................

[P.S.: Kudos Nats -- > ιδέα για repo-κατάσταση!]

*Note:

Java-ish rationale for JUST 1 public class per file, can be somehow implemented as a concept in Python as well (duh!), as classes packages organised in seperate files.

TO BE GOOGLED:

empty __init__.py file

multi file packages Vs one big module

all modules in one folder, appended this path to os.path

http://pydoit.org/ -- The Story: https://schettino72.wordpress.com/2008/04/14/doit-a-build-tool-tale/

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