Grad School Advice

http://web.pdx.edu/~obrienk/gradschool%20advice.pdf

http://www.howigotintostanford.com/shorter.php

http://www.justcolleges.com/grad/why-graduate-school.htm

http://www.military.com/finding-a-school/why-graduate-school

http://genomemedicine.com/content

http://www.biomedcentral.com/bmcbioinformatics?page=2&relevant=true

One of the great challenges of plant research is protecting crops from pathogenic attack. Soderlund describes a range of large-scale experimental studies for the analysis of plant–pathogen interactions, describing the bioinformatics approaches and tools that have been influential in gaining new knowledge from these data sets. She distinguishes between those problems that may be solved using generic bioinformatics methodologies and those for which bespoke solutions are required. Soderlund discusses key resources including fungal and oomycete databases and initiatives such as the PAMGO ontology, emphasizing the role of improved web-based resources in the knowledge discovery process.

Next, we shift our focus towards the important theme of understanding the genetic basis of plant traits. Zhang et al. describe methodologies for inferring genotype–phenotype associations through whole genome studies, focusing on the use of mixed model. The authors show that although many parts of the analytical process are rooted in animal studies and while some transfer directly to plant studies, others do not and need to be adapted. They discuss challenges to software engineering of the various methodologies involved, particularly in light of new high-throughput marker data sets, and discuss a number of software tools with regard to many key user requirements.

Finally, Lysenko et al. describe a problem that has arisen due to our capability to develop the data sets such as those described throughout this issue, that of developing software systems to integrate across information resources. The authors describe some of the problems inherent to data integration and discuss and contrast methods which are being used to solve them, while also noting the benefits of such approaches in generating new knowledge. They discuss two distinct, plant-oriented data integration case studies in the model plant Arabidopsis thaliana with reference to their own Ondex system, and look to new challenges in the form of high-throughput data sets, standards and ontologies.
http://bib.oxfordjournals.org/cgi/content/full/10/6/593

Disease to gene

http://bib.oxfordjournals.org.proxy.lib.sfu.ca/cgi/content/full/10/6/654
The Fungal Genome Initiative (FGI) [3] was formed to sequence fungal genomes,

Therefore, various software packages have been developed to partition the ESTs based on their nucleotide composition. Huitema et al. [35] used the fact that the GC content can sometimes be quite different between the host and pathogen. Maor et al. [36] developed the PF-IND method that uses codon usage bias. The problem with using codon usage is the need to find the in-frame coding sequence, and the fact that ESTs are also composed of untranslated regions. Therefore, Emmersen et al. [37] used triplet frequencies which are computed over both the coding and non-coding regions. The triplet frequencies are calculated using a sliding window over a training set of plant and pathogen ESTs, and a SVM (support vector machine) is used for the classifications.