The lab that knows why you’re so busy

Nature 526, 7574 (2015). http://www.nature.com/doifinder/10.1038/526492a

Author: Helen Pearson

Why does modern life seem so hectic? An Oxford centre is trying to find answers with the world's biggest collection of time-use diaries.

Background: ChIP-seq experiments are widely used to detect and study DNA-protein interactions, such as transcription factor binding and chromatin modifications. However, downstream analysis of ChIP-seq data is currently restricted to the evaluation of signal intensity and the detection of enriched regions (peaks) in the genome. Other features of peak shape are almost always neglected, despite the remarkable differences shown by ChIP-seq for different proteins, as well as by distinct regions in a single experiment. Results: We hypothesize that statistically significant differences in peak shape might have a functional role and a biological meaning. Thus, we design five indices able to summarize peak shapes and we employ multivariate clustering techniques to divide peaks into groups according to both their complexity and the intensity of their coverage function. In addition, our novel analysis pipeline employs a range of statistical and bioinformatics techniques to relate the obtained peak shapes to several independent genomic datasets, including other genome-wide protein-DNA maps and gene expression experiments. To clarify the meaning of peak shape, we apply our methodology to the study of the erythroid transcription factor GATA-1 in K562 cell line and in megakaryocytes. Conclusions: Our study demonstrates that ChIP-seq profiles include information regarding the binding of other proteins beside the one used for precipitation. In particular, peak shape provides new insights into cooperative transcriptional regulation and is correlated to gene expression.

Background: Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. Methods: We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. Results: The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. Conclusion: Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

Background: Recently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments. Results: In this paper, we compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish). The experiments were based on real Illumina high-throughput RNA-Seq of 35- and 76-nucleotide sequences produced in the MAQC project and simulation reads. Reads were mapped with human genome obtained from UCSC Genome Browser Database. For precise evaluation, we investigated Spearman correlation between the normalization results from RNA-Seq and MAQC qRT-PCR values for 996 genes. Based on this work, we showed that out of the eight non-abundance estimation normalization methods, RC, UQ, Med, TMM, DESeq, and Q gave similar normalization results for all data sets. For RNA-Seq of a 35-nucleotide sequence, RPKM showed the highest correlation results, but for RNA-Seq of a 76-nucleotide sequence, least correlation was observed than the other methods. ERPKM did not improve results than RPKM. Between two abundance estimation normalization methods, for RNA-Seq of a 35-nucleotide sequence, higher correlation was obtained with Sailfish than that with RSEM, which was better than without using abundance estimation methods. However, for RNA-Seq of a 76-nucleotide sequence, the results achieved by RSEM were similar to without applying abundance estimation methods, and were much better than with Sailfish. Furthermore, we found that adding a poly-A tail increased alignment numbers, but did not improve normalization results. Conclusion: Spearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.

Background: Functional annotation of genes and gene products is a major challenge in the post-genomic era. Nowadays, gene function curation is largely based on manual assignment of Gene Ontology (GO) annotations to genes by using published literature. The annotation task is extremely time-consuming, therefore there is an increasing interest in automated tools that can assist human experts. Results: Here we introduce GOTA, a GO term annotator for biomedical literature. The proposed approach makes use only of information that is readily available from public repositories and it is easily expandable to handle novel sources of information. We assess the classification capabilities of GOTA on a large benchmark set of publications. The overall performances are encouraging in comparison to the state of the art in multi-label classification over large taxonomies. Furthermore, the experimental tests provide some interesting insights into the potential improvement of automated annotation tools. Conclusions: GOTA implements a flexible and expandable model for GO annotation of biomedical literature. The current version of the GOTA tool is freely available at http://gota.apice.unibo.it.

Background: A rapidly growing amount of knowledge about signaling and gene regulatory networks is available in databases such as KEGG, Reactome, or RegulonDB. There is an increasing need to relate this knowledge to high-throughput data in order to (in)validate network topologies or to decide which interactions are present or inactive in a given cell type under a particular environmental condition. Interaction graphs provide a suitable representation of cellular networks with information flows and methods based on sign consistency approaches have been shown to be valuable tools to (i) predict qualitative responses, (ii) to test the consistency of network topologies and experimental data, and (iii) to apply repair operations to the network model suggesting missing or wrong interactions. Results: We present a framework to unify different notions of sign consistency and propose a refined method for data discretization that considers uncertainties in experimental profiles. We furthermore introduce a new constraint to filter undesired model behaviors induced by positive feedback loops. Finally, we generalize the way predictions can be made by the sign consistency approach. In particular, we distinguish strong predictions (e.g. increase of a node level) and weak predictions (e.g., node level increases or remains unchanged) enlarging the overall predictive power of the approach. We then demonstrate the applicability of our framework by confronting a large-scale gene regulatory network model of Escherichia coli with high-throughput transcriptomic measurements. Conclusion: Overall, our work enhances the flexibility and power of the sign consistency approach for the prediction of the behavior of signaling and gene regulatory networks and, more generally, for the validation and inference of these networks

Corrigendum: A basal ichthyosauriform with a short snout from the Lower Triassic of China

Nature 527, 7579 (2015). doi:10.1038/nature15533

Authors: Ryosuke Motani, Da-Yong Jiang, Guan-Bao Chen, Andrea Tintori, Olivier Rieppel, Cheng Ji & Jian-Dong Huang

Nature517, 485–488 (2015); doi:10.1038/nature13866The data matrix in the original Supplementary Data 3 of this Letter reproduced the tree topology shown in Extended Data Fig. 3 but the accompanying character descriptions did not match the coding given

Corrigendum: Influence maximization in complex networks through optimal percolation

Nature 527, 7579 (2015). doi:10.1038/nature15737

Authors: Flaviano Morone & Hernán A. Makse

Nature524, 65–68 (2015); doi:10.1038/nature14604In the Acknowledgements section of this Letter, ‘ARL’ should read ‘Army Research Laboratory Cooperative Agreement Number W911NF-09-2-0053 (the ARL Network Science CTA)’. This has been corrected in the online versions of the paper.

Retraction: Non-blinking semiconductor nanocrystals

Nature 527, 7579 (2015). doi:10.1038/nature15745

Authors: Xiaoyong Wang, Xiaofan Ren, Keith Kahen, Megan A. Hahn, Manju Rajeswaran, Sara Maccagnano-Zacher, John Silcox, George E. Cragg, Alexander L. Efros & Todd D. Krauss

Nature459, 686–689 (2009); doi:10.1038/nature08072In this Letter, we reported the unusual non-blinking characteristics of the fluorescence from individual CdZnSe/ZnSe alloyed quantum dots. However, it has recently come to our attention that similar fluorescence behaviour was seen by

Burst bubbles

Nature 526, 7575 (2015). doi:10.1038/526609b

Two medical-technology companies illustrate the ups and downs of innovation.

Ghost story

Nature 526, 7575 (2015). doi:10.1038/526610a

The problem of abandoned fishing gear and its effects on marine life deserve greater attention.

Forensic DNA evidence is not infallible

Nature 526, 7575 (2015). http://www.nature.com/doifinder/10.1038/526611a

Author: Cynthia M. Cale

As DNA analysis techniques become more sensitive, we must be careful to reassess the probabilities of error, argues Cynthia M. Cale.

Zoology: Bright light as sex signal

Nature 526, 7575 (2015). doi:10.1038/526612a

Brighter female glow-worms lay more eggs than their dim rivals and are more attractive to potential nocturnal mates.Juhani Hopkins at the University of Oulu in Finland and his colleagues allowed 26 female glow-worms (Lampyris noctiluca; pictured) to mate in the lab.

Neuroscience: Alzheimer's clue from spatial test

Nature 526, 7575 (2015). doi:10.1038/526612b

Young adults who are at increased risk of developing Alzheimer's disease show abnormal function in a part of the brain involved in spatial navigation.Nikolai Axmacher at Ruhr University Bochum in Germany and his colleagues used neuroimaging to measure the functioning of the 'grid-cell' system

Astronomy: Red-giant rogue in Andromeda

Nature 526, 7575 (2015). doi:10.1038/526612c

Astronomers have spotted a giant 'runaway star' speeding through the Andromeda galaxy; the first of its kind seen outside the Milky Way.Whereas most stars flow together around the centre of their galaxy, some, known as runaways, travel at different directions and speeds to their

Materials: Iron skin senses the softest touch

Nature 526, 7575 (2015). doi:10.1038/526612d

An iron-based artificial skin can sense the lightest touch.Ahmed Alfadhel and Jürgen Kosel at the King Abdullah University of Science and Technology in Thuwal, Saudi Arabia, made a tactile sensor by embedding iron nanowires in hair-like structures called cilia, made of a polymer called

Genomics: Gene regulation predates animals

Nature 526, 7575 (2015). doi:10.1038/526612e

The oldest ancestor of animal life used the same tricks that modern humans do to turn genes on and off.Alex de Mendoza at the Institute of Evolutionary Biology in Barcelona, Spain, and his colleagues studied gene regulation in the fungus-like single-celled organism Creolimax fragrantissima

Disease: Plague is an ancient pathogen

Nature 526, 7575 (2015). doi:10.1038/526613a

Plague was plaguing humanity thousands of years earlier than previously thought, but in a less transmissible form.Yersinia pestis bacteria, which are thought to have been behind the Black Death that killed millions in the fourteenth century, have previously been found in burial sites

Stem cells: Molecular menu creates neurons

Nature 526, 7575 (2015). doi:10.1038/526613b

Astrocyte cells in the brain can be reprogrammed into neurons using a precise sequence of molecules. The technique may one day be useful in brain repair.Similar cells have previously been reprogrammed into neurons using viruses, but Gong Chen and Gang-Yi Wu at Pennsylvania State

Lab tools: Superconducting sensors warm up

Nature 526, 7575 (2015). doi:10.1038/526613c

An extremely sensitive, superconductor-based magnetic sensor can work at around 77 kelvin, a temperature achievable with liquid nitrogen rather than the expensive liquid helium required by typical existing devices, which operate at just above absolute zero.Superconducting quantum-interference devices (SQUIDs) can sense individual quanta of