Nature Biotechnology - CURRENT ISSUE : August 2008 - Vol 26 No 8
- Second-generation antibody-drug conjugates
- Fine-tuning reprogramming
- Sequencing matches microRNAs to mRNA targets
LATEST HIGHLIGHTS
ADVANCE ONLINE PUBLICATION
Predicting peptide-protein domain interactions
Letter by Chen et al.PDZ domains represent one of the largest families of interaction domains. Chen et al. develop a scoring matrix that enables prediction of peptide-PDZ domain interactions. Unlike previous methods, the model works to some extent for PDZ domains that were not part of the training set.
ADVANCE ONLINE PUBLICATION<
Targeted metagenomics by isotope labeling
Letter by Kalyuzhnaya et al.Metagenomics, or shotgun sequencing of environmental DNA, is used to study complex microbial communities. Kalyuzhnaya et al. describe a method for targeting specific microbial subpopulations in environmental samples and use it to analyze microbes that metabolize C1 compounds.
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CURRENT ISSUE
Small molecule enhances RNAi activity
Article by Shan et al.Little is known about the regulation of RNA interference (RNAi). Shan et al. construct a reporter system to monitor RNAi activity and identify a small molecule that increases RNAi by facilitating the interaction between small RNAs and a protein involved in small RNA loading and processing.
CURRENT ISSUE
Plant-parasitic nematode genome
Article by Abad et al. - this article is FREEBiological control of the root-knot nematode Meloidogyne incognita, one of the world’s most destructive crop pathogens, presents a major opportunity for safely improving global agricultural yields. Its 86-Mb genome—the first to be sequenced for a strictly parthenogenetic species—provides a blueprint to design new strategies for plant protection.
Impact Factor
The 2007 impact factor for Nature Biotechnology is 22.8, according to the ISI Journal Citation Reports. This places Nature Biotechnology first among primary research journals in the field of biotechnology.
Special Section
The expectation maximization algorithm
Primer by Do & Batzoglou
- this article is FREE
The expectation maximization algorithm estimates parameters for probabilistic models based on incomplete data. Do and Batzoglou describe how it works and how it can build and train models for applications such as gene expression clustering, motif finding and haplotype inference.
