A paper on next generation sequencing

Exome localization of complex disease association signals

Benjamin Lehne, Cathryn M Lewis and Thomas Schlitt

http://www.biomedcentral.com/1471-2164/12/92/

Biologists' view on genomics

View from different angles on genomics.

Epigenomics tutoial

Here are the 3 video tutorials from NCBI site. The link of the first video on the playlist is : http://www.youtube.com/watch?v=n68QAqHlcv8&feature=BFa&list=PL5325E662E57F9E7F

Learning R and Bioconductor

R is a powerful statistical tool which is heavily used in bioinformatics. Bioconductor, a very useful tool for high throughput genomic data analysis, has been developed using R. Here are two important links for learning R and Bioconductor.

1. http://www.cyclismo.org/tutorial/R/index.html
2. http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual

Besides, the lectures by Dr. Roger D Peng, Associate Professor, Johns Hopkins University in the Computing for Data Analysis course are very helpful. The lectures are available in youtube - http://www.youtube.com/watch?v=EiKxy5IecUw&list=PL7Tw2kQ2edvr2lv8FTvg9msf8YHQz0MS0

Happy learning!

Preprocessing of microarray data

Normalization:

When microarray data is obtained from multiple arrays, it is necessary to normalize the dataset to avoid variation due to different environments. There are several normalization techniques available in the literature. For example, Lowess normalization, Quantile normalization etc. Among these, quantile normalization is the current favorite method applied on microarray analysis.

Transformation:

Besides normalization, it is also beneficial to transform the data to correctly treat both up- and down-regulated data. The most widely used transformation technique is the logarithmic base 2. Notably, logarithms treat numbers and their reciprocals symmetrically. For example: log₂(1) = 0, log₂(2) = 1, log₂(1/2) = -1, log₂(4) = 2, log₂(1/4) = -2.

Filtering:

If the intensity of hybridization in microarray is low (close to the background), then usually relative error becomes high. The common practice is to filter out (discard) the array elements which are statistically significantly different from the background.

References:

1. Slonim DK, Yanai I (2009) Getting Started in Gene Expression Microarray Analysis. PLoS Comput Biol 5(10): e1000543. doi:10.1371/journal.pcbi.1000543
2. Quackenbush, J. (2002) Microarray data normalization and transformation. Nature Genetics. Vol.32 supplement pp496-501.