Tag Archive for 'RNA sequencing'

Going green: lessons from plant genomics for human sequencing studies

This is a guest post by Jeffrey Rosenfeld. Jeff is a next-generation sequencing advisor in the High Performance and Research Computing group at the University of Medicine and Dentistry of New Jersey, working on a variety of human and microbial genetics projects. He is also a Visiting Scientist at the American Museum of Natural History where he focuses on whole-genome phylogenetics. He was trained at the University of Pennsylvania, New York University and Cold Spring Harbor Laboratory.

As human geneticists, it is all too easy to ignore papers published about non-human organisms – especially when those organisms are plants. After all, how much can the analysis of (say) Arabidopsis genome diversity possibly assist in my quest to better understand the human genome and determine which genes cause disease? Quite a bit, as it happens: a fascinating recent paper in Nature demonstrates a number of lessons that we can learn from our distant green relatives.

By exploiting the small genome size of Arabidopsis (~120 million bases, compared to the relatively gargantuan 3 billion bases of Homo sapiens), researchers were able to perform complete genome sequencing and transcriptome profiling in 18 different ecotypes of the plant (similar to what we would call strains of an animal).

In a normal genome re-sequencing experiment, the procedure is to obtain DNA from an individual, sequence the DNA, align it to a reference sequence and then to call variants (i.e. differences from the reference). This approach is used by the 1000 Genomes Project and basically all of the hundreds of disease-focused human sequencing projects currently underway around the world. This approach allows researchers to relatively easily identify single-base substitution (SNP) and small insertion/deletion (indel) differences between genomes. However, the amount of variability that can be identified is restricted by the use of a reference: regions where there is extreme divergence between the reference and sample genomes are often badly called, and more complex variants (e.g. large, recurrent rearrangements of DNA) can be missed. Additionally, and crucially, sequences that are not present in the reference genome will be completely missed by this approach.
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Notes on the evidence for extensive RNA editing in humans

UPDATE 3/17/12: A more extensive analysis of the paper discussed in this post is here. Several groups have concluded that at least 90% of the sites identified are technical artifacts

The “central dogma” of molecular biology holds that the information present in DNA is transferred to RNA and then to protein. In a paper published online at Science yesterday, Li and colleagues report a potentially extraordinary observation: they show evidence that, within any given individual, there are tens of thousands of places where transcribed RNA does not match the template DNA from which it is derived [1]. This phenomenon, called RNA editing, is generally thought to be limited (in humans) to conversions of the base adenosine to the base inosine (which is read as guanine by DNA sequencers), and occasionally from cytosine to uracil. In contrast, these authors report that any type of base can be converted to any other type of base.

If these observations are correct, they represent a fundamental change in how we view the process of gene regulation. However, in this post I am going to point out a couple of technical issues that, if not properly taken into account, have the potential to cause a large number of false positives in this type of data. The main point can be summarized like this: RNA editing involves the production of two different RNA and/or protein sequences from a single DNA sequence. To infer RNA editing from the presence of two different RNA and/or protein sequences, then, one must be very sure that they derive from the same DNA sequence, rather than from two different copies of the DNA (due to, for example, paralogs or copy number variants). Although this issue has the potential to be a large source of false positives in a study like this, I will discuss an additional technical problem that could also result in false positives.

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The cell is a messy place: understanding alternative splicing with RNA sequencing

Though this site is largely dedicated to discussions of personal genomics, I’d like to use this post to discuss some of my recent work (done with Athma Pai, Yoav Gilad, and Jonathan Pritchard) on mRNA splicing. Our paper, in which we argue that splicing is a relatively error-prone and noisy process, has just been published in PLoS Genetics [1].

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