gpcr

1. Deanna's biostars post
2. MAPT alt locus
3. MAPT under selection in Eurpeans
4. Zody/Eichler MAPT Inversion toggling
5. The MAPT locus—a genetic paradigm in disease susceptibility
6. Case study for reporting variants in the VCF format.
7. http://content.csbs.utah.edu/~rogers/ant6299/readings/Hardy-BST-33-582.pdf
8. Kitzman/Eichler show that sample NA10847 is heterozygous H1/H2 http://www.nature.com.proxy.its.virginia.edu/nbt/journal/v29/n1/extref/nbt.1740-S1.pdf
9. Terminology

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dad	mom	kid	Inheritance description
HOM_REF	HOM_REF	HOM_REF	Expected
HOM_REF	HOM_REF	HET	Mendelian violation (plausible de novo)
HOM_REF	HOM_REF	HOM_ALT	Mendelian violation (implausible de novo)
HOM_REF	HOM_ALT	HOM_REF	Mendelian violation (uniparental disomy)
HOM_REF	HOM_ALT	HET	Expected
HOM_REF	HOM_ALT	HOM_ALT	Mendelian violation (uniparental disomy)
HOM_REF	HET	HOM_REF	Expected
HOM_REF	HET	HET	Expected

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#!/usr/bin/env python
import sys
import pprint

def make_grantham_dict(grantham_mat_file):

        """
	Citation:   http://www.ncbi.nlm.nih.gov/pubmed/4843792
	Provenance: http://www.genome.jp/dbget-bin/www_bget?aaindex:GRAR740104

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# slide 4
curl https://s3.amazonaws.com/gemini-tutorials/trio.trim.vep.vcf.gz > trio.trim.vep.vcf.gz
curl https://s3.amazonaws.com/gemini-tutorials/recessive.ped > dominant.ped
gemini load --cores 4 \
              -v trio.trim.vep.vcf.gz \
              -t VEP \
              --skip-gene-tables \
              -p dominant.ped \
         trio.trim.vep.dominant.db

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Drag a rectangle around an area of interest to zoom in.

If anyone is interested in a more general version (to take in custom data), or would like me to correct something, leave a comment on the gist.

Gist

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#' When plotting multiple data series that share a common x axis but different y axes,
#' we can just plot each graph separately. This suffers from the drawback that the shared axis will typically
#' not align across graphs due to different plot margins.
#' One easy solution is to reshape2::melt() the data and use ggplot2's facet_grid() mapping. However, there is
#' no way to label individual y axes.
#' facet_grid() and facet_wrap() were designed to plot small multiples, where both x- and y-axis ranges are
#' shared acros all plots in the facetting. While the facet_ calls allow us to use different scales with
#' the \code{scales = "free"} argument, they should not be used this way.
#' A more robust approach is to the grid package grid.draw(), rbind() and ggplotGrob() to create a grid of 
#' individual plots where the plot axes are properly aligned within the grid.

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"""
Multiclass SVMs (Crammer-Singer formulation).

A pure Python re-implementation of:

Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex.
Mathieu Blondel, Akinori Fujino, and Naonori Ueda.
ICPR 2014.
http://www.mblondel.org/publications/mblondel-icpr2014.pdf
"""

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## data input (number of reads mapped to each category)
total=100
rRNA=5 # mapped to nuclear rRNA regions
mtRNA=7  # mapped to mitochondria genome
# for the rest of above, then we divide into different category, like http://www.biomedcentral.com/1741-7007/8/149 did.
intergenic=48 
introns=12
exons=30
upstream=3
downstream=6

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==========================================          ==========================================
             TMUX COMMAND                                        WINDOW (TAB)
==========================================          ==========================================

List    tmux ls                                     List         ^b w
New          -s <session>                           Create       ^b c
Attach       att -t <session>                       Rename       ^b , <name>
Rename       rename-session -t <old> <new>          Last         ^b l               (lower-L)
Kill         kill-session -t <session>              Close        ^b &