Worth reading: Goodbye academia, I get a life.

Amazonia! – explore the jungle of microarray results

Paradoxically, the tremendous downpour of microarray results prevents a simple use of expression data. Therefore, we propose a thematic entry to public transcriptomes: you may for instance query a gene on a “Stem Cells page”, where you will see the expression of your favorite gene across selected microarray experiments related to stem cell biology. This selection of samples can be customized at will among the 6462 samples currently present in the database.

Every transcriptome study results in the identification of lists of genes relevant to a given biological condition. In order to include this valuable information in any new query in the Amazonia! database, we indicate for each gene in which lists it is included. This is a straightforward and efficient way to synthesize hundreds of microarray publications.

A special feature of Amazonia! is the field of human stem cells, notably embryonic stem cells.

This overview presentation is two years old, but still a highly valuable resource: modules and tools mentioned are alive and useful.
I think this is the second presentation by Giovanni I’m embedding (first one being about GNU/make for bioinformatics).

Imagine you need to get a few lines from a group of files with missing identifier mappings. I have a bunch of files with content similar to this one:

ENSRNOG00000018677 1368832_at 25233
ENSRNOG00000002079 1369102_at 25272
ENSRNOG00000043451 25353
ENSRNOG00000001527 1388013_at 25408
ENSRNOG00000007390 1389538_at 25493

In the example above I need '25353', which does not have corresponding affy_probeset_id in the 2nd column.

It is clear how to do that:

This outputs a column of required IDs (EntrezGene in this example):

116720
679845
309295
364867
298220
298221
25353

However, I need these IDs as a comma-separated list, not as newline-separated list.

There are several ways to achieve the desired result (only the last pipe commands differ):

These solutions differ in efficiency and (slightly) in output. sed will read all the input into its buffer to replace newlines with other separators, so it might not be best for large files. tr might be the most efficient, but I haven't tested that. paste will re-use delimiters, so you cannot really get comma-space ", " separation with it.

Sources: linuxquestions 1 (explains used sed commands), linuxquestions 2, nixcraft.

Superimposing gene expression data onto pathways from databases is a common task in the final steps of microarray data analysis - that is, biological interpretation and results discussion.

I have found many tools which claim to facilitate this procedure. Some of them are reviewed below (in no specific order).
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1. Install the annotationTools R package:
   source("http://bioconductor.org/biocLite.R")
   biocLite("annotationTools")
2. Download full HomoloGene data file from ftp://ftp.ncbi.nlm.nih.gov/pub/HomoloGene/current
3. library(annotationTools)
4. homologene = read.delim("homologene.data", header=FALSE)
5. mygenes = read.table("file with one entrez ID of the source organism per line.txt")
6. getHOMOLOG(unlist(mygenes), taxonomy_ID_of_target_organism, homologene) [alternatively, wrap the call to getHOMOLOG into unlist to get a vector]

It might be easier to achieve the same results with a Perl script calling NCBI's e-utils.

Nature published the said survey based on responses of over 10000 employees in science. It has lots of multi-axis data to explore, and some major trends are discussed in the special report. Highly recommended for anyone considering science career changes.