Correction: Evolutionary principles of modular gene regulation in yeasts

[This corrects the article DOI: 10.7554/eLife.00603.].

Applications of metabolomics in cancer research

The first discovery of metabolic changes in cancer occurred almost a century ago. While the genetic underpinnings of cancer have dominated its study since then, altered metabolism has recently been acknowledged as a key hallmark of cancer and metabolism-focused research has received renewed attention. The emerging field of metabolomics – which attempts to profile all metabolites within a cell or biological system – is now being used to analyze cancer metabolism on a system-wide scale, painting a broad picture of the altered pathways and their interactions with each other. While a large fraction of cancer metabolomics research is focused on finding diagnostic biomarkers, metabolomics is also being used to obtain more fundamental mechanistic insight into cancer and carcinogenesis. Applications of metabolomics are also emerging in areas such as tumor staging and assessment of treatment efficacy. This review summarizes contributions that metabolomics has made in cancer research and presents the current challenges and potential future directions within the field.

Systematic identification of conserved metabolites in GC/MS data for metabolomics and biomarker discovery

Analysis of metabolomic profiling data from gas chromatography-mass spectrometry (GC/MS) measurements usually relies upon reference libraries of metabolite mass spectra to structurally identify and track metabolites. In general, techniques to enumerate and track unidentified metabolites are nonsystematic and require manual curation. We present a method and software implementation, freely available at http://spectconnect.mit.edu, that can systematically detect components that are conserved across samples without the need for a reference library or manual curation. We validate this approach by correctly identifying the components in a known mixture and the discriminating components in a spiked mixture. Finally, we demonstrate an application of this approach with a brief analysis of the Escherichia coli metabolome. By systematically cataloguing conserved metabolite peaks prior to data analysis methods, our approach broadens the scope of metabolomics and facilitates biomarker discovery.

A generic motif discovery algorithm for sequential data

MOTIVATION

Motif discovery in sequential data is a problem of great interest and with many applications. However, previous methods have been unable to combine exhaustive search with complex motif representations and are each typically only applicable to a certain class of problems.

RESULTS

Here we present a generic motif discovery algorithm (Gemoda) for sequential data. Gemoda can be applied to any dataset with a sequential character, including both categorical and real-valued data. As we show, Gemoda deterministically discovers motifs that are maximal in composition and length. As well, the algorithm allows any choice of similarity metric for finding motifs. Finally, Gemoda's output motifs are representation-agnostic: they can be represented using regular expressions, position weight matrices or any number of other models for any type of sequential data. We demonstrate a number of applications of the algorithm, including the discovery of motifs in amino acids sequences, a new solution to the (l,d)-motif problem in DNA sequences and the discovery of conserved protein substructures.

AVAILABILITY

Gemoda is freely available at http://web.mit.edu/bamel/gemoda

An extension and novel solution to the (l,d)-motif challenge problem

The (l,d)-motif challenge problem, as introduced by Pevzner and Sze, is a mathematical abstraction of the DNA functional site discovery task. Here we expand the (l,d)-motif problem to more accurately model this task and present a novel algorithm to solve this extended problem. This algorithm is guaranteed to find all (l,d)-motifs in a set of input sequences with unbounded support and length. We demonstrate the performance of the algorithm on publicly available datasets and show that the algorithm deterministically enumerates the optimal motifs.