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Chang Li-Wei et al. , An integrated approach to characterize transcription factor and microRNA regulatory networks involved in Schwann cell response to peripheral nerve injury Human and rodent genomics. BMC Genomics (2013) doi:10.1186/1471-2164-14-84 Merelli Ivan et al. , SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS Computational Intelligence in Bioinformatics and Biostatistics: new trends from the CIBB conference series Seventh International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2010). BMC Bioinformatics (2013) doi:10.1186/1471-2105-14-S1-S9
Introduction UCSC Genome Browser: The basics – an overview Basic searches in the browser The basic displays: An overview Displaying data types: Visual cues Setting the layout with menu controls
For beginners in the field, this review highlights the key features of the genome browser at UCSC for data display, and provides nearly step-by-step procedures for creating publication quality maps. The browser offers an engine (Blat) for searching a known genomic DNA for correspondence with protein and DNA sequences specified by the user. The results provide links to graphical displays, known as maps. Users can create “designer maps” by adding Tracks to view various types of data and specific landmarks. The browser offers an extensive list of options. They include the position of annotated genes, the position of reference cDNA sequences (RefSeq from GenBank), the position of alternatively spliced mRNA species, and predictions derived from computational models to identify potential transcription start sites and potential protein binding elements in genomic DNA.
Filter your results @ GenoScapeGC GenoScapeGC GenoScapeGC Excellent piece by @ Erika_Check DNA has limits, but so does study questioning its value, geneticists say http://t.co/maZQWwfa DNA has limits, but so does study questioning its value, geneticists say : Nature News Blog Scientists are irked over a paper claiming, as The New York Times reported on Monday, that " DNA's power to predict illness is limited."
What the ‘limits of DNA’ story reveals about the challenges of science journalism in the ‘big data’ ageBy Erika Check Hayden | April 6, 2012 | 30 Comments As a science journalist, I sympathize with book reviewers who wrestle with the question of whether to write negative reviews. It seems a waste of time to write about a dog of a book when there are so many other worthy ones; but readers deserve to know if Oprah is touting a real stinker. On 2 April, Science Translational Medicine published a study on DNA’s shortcomings in predicting disease. My editors and I had decided not to cover the study last week after we saw it in the journal’s embargoed press packet, because my sources offered heavy critiques of its methods.
Breakdown of the number of loss-of-function variants in a "typical" genome I don’t normally blog here about my own research, but I’m making an exception for this paper . There are a few reasons to single this paper out: firstly, it’s in Science (!); and secondly, no fewer than five Genomes Unzipped members (me, Luke, Joe, Don and Jeff) are co-authors. For me it also represents the culmination of a fantastic postdoc position at the Wellcome Trust Sanger Institute (for those who haven’t heard on Twitter, I’ll be starting up a new research group at Massachusetts General Hospital in Boston next month). Readers who don’t have a Science subscription can access a pre-formatted version of the manuscript here .
Background Cryptic genetic variation (CGV) is defined as “standing genetic variation that does not contribute to the normal range of phenotypes observed in a population, but that is available to modify a phenotype that arises after environmental change or the introduction of novel alleles” [Gibson & Dworkin, 2004]. As such, CGV fills the gap between : 1. expressed genetic variation , defined as genetic variation that contributes to the normal range of phenotypes actually present in a population ; 2. neutral genetic variation , that does not contribute to phenotypes under any likely genetic or environmental conditions ; a typical example of neutral genetic variation would be synonymous substitutions in protein coding sequences.
Symposium: Personalised medicine and pharmacogenetics, pharmacogenomics Chair 1: Martin Kennedy (New Zealand) Chair 2: Y T Chen (Taiwan) 1100 – 1145 Translation of pharmacogenetics into the clinic: The immunogenetics example Elizabeth Phillips (Australia)
Bits and Base Pairs A reflection on bits and base pairs. Role: Everything Date: Summer 2011 Music Credit: Apollo by Danger Beach Recognition: The Atlantic , Time , Pop!Tech , OWNI About I was playing around with some Processing I wrote earlier and decided to turn it into a short.
Professor Dame Janet Thornton, Director The European Bioinformatics Institute is part of EMBL , Europe’s flagship laboratory for the life sciences. EMBL-EBI provides freely available data from life science experiments covering the full spectrum of molecular biology. While we are best known for our provision of bioinformatics services, about 20% of our institute is devoted to basic research .
One of the new things coming in Biopython 1.59 is improved chromosome diagrams, something you may have seen via Twitter . I’ve just been updating the Biopython Tutorial (current version here , PDF ) to include an example drawing this: Here’s a PDF version too.
A current lecture course surveying genomics and bioinformatics is available online, hosted on YouTube by GenomeTV . Handouts for the thirteen week course are hosted on the course website . We’re told that the course includes an update on technologies that have changed over the past two years. These lectures are introductory. They are aimed at biologists who wish to learn more about genomics or bioinformatics, perhaps because their upcoming work intersects with it, rather those who already have some detailed knowledge or experience of the field. I would add that, being lectures, they are high-flying in the sense that they do not deal with the actual hands-on work involved, which introduces finer detail and further issues not covered in these lectures.
The sequencing of the human genome has changed how we do genetics. Instead of examining one gene at a time, we now look at the genetic variation across the entire genome. We can examine how transcription changes at a global level in different cell types, or under different conditions. How do these methods that examine differences in "global" transcriptional, translational and epigenetic phenomena affect how we think about human genetics?
Genomes can be aligned to each other in order to study their evolution, to find homologs, and to determine the location of potentially functional genomic regions. In this section, you will learn about functional elements predicted from conservation data as well as experimentally-defined regulatory elements. You will also learn how to order a clone for use in biological experiments to further study a gene of interest. Conservation and Regulation Data
Asked how mature the field of genome assembly is, Ian Korf at the University of California, Davis, compares it to a teenager with great capabilities. “It's got bold assertions about what it can do, but at the same time it's making embarrassing mistakes,” he says. Perhaps the biggest barrier to maturity is that there are few ways to distinguish true insight from foolish gaffe. When a species' genome is newly assembled, no one knows what's real, what's missing, and what's experimental artifact.