35 research outputs found
Differentiating Protein-Coding and Noncoding RNA: Challenges and Ambiguities
The assumption that RNA can be readily classified into either protein-coding or non-protein–coding categories has pervaded biology for close to 50 years. Until recently, discrimination between these two categories was relatively straightforward: most transcripts were clearly identifiable as protein-coding messenger RNAs (mRNAs), and readily distinguished from the small number of well-characterized non-protein–coding RNAs (ncRNAs), such as transfer, ribosomal, and spliceosomal RNAs. Recent genome-wide studies have revealed the existence of thousands of noncoding transcripts, whose function and significance are unclear. The discovery of this hidden transcriptome and the implicit challenge it presents to our understanding of the expression and regulation of genetic information has made the need to distinguish between mRNAs and ncRNAs both more pressing and more complicated. In this Review, we consider the diverse strategies employed to discriminate between protein-coding and noncoding transcripts and the fundamental difficulties that are inherent in what may superficially appear to be a simple problem. Misannotations can also run in both directions: some ncRNAs may actually encode peptides, and some of those currently thought to do so may not. Moreover, recent studies have shown that some RNAs can function both as mRNAs and intrinsically as functional ncRNAs, which may be a relatively widespread phenomenon. We conclude that it is difficult to annotate an RNA unequivocally as protein-coding or noncoding, with overlapping protein-coding and noncoding transcripts further confounding this distinction. In addition, the finding that some transcripts can function both intrinsically at the RNA level and to encode proteins suggests a false dichotomy between mRNAs and ncRNAs. Therefore, the functionality of any transcript at the RNA level should not be discounted
Search for Kaluza-Klein Graviton Emission in Collisions at TeV using the Missing Energy Signature
We report on a search for direct Kaluza-Klein graviton production in a data
sample of 84 of \ppb collisions at = 1.8 TeV, recorded
by the Collider Detector at Fermilab. We investigate the final state of large
missing transverse energy and one or two high energy jets. We compare the data
with the predictions from a -dimensional Kaluza-Klein scenario in which
gravity becomes strong at the TeV scale. At 95% confidence level (C.L.) for
=2, 4, and 6 we exclude an effective Planck scale below 1.0, 0.77, and 0.71
TeV, respectively.Comment: Submitted to PRL, 7 pages 4 figures/Revision includes 5 figure
Measurement of the average time-integrated mixing probability of b-flavored hadrons produced at the Tevatron
We have measured the number of like-sign (LS) and opposite-sign (OS) lepton
pairs arising from double semileptonic decays of and -hadrons,
pair-produced at the Fermilab Tevatron collider. The data samples were
collected with the Collider Detector at Fermilab (CDF) during the 1992-1995
collider run by triggering on the existence of and candidates
in an event. The observed ratio of LS to OS dileptons leads to a measurement of
the average time-integrated mixing probability of all produced -flavored
hadrons which decay weakly, (stat.)
(syst.), that is significantly larger than the world average .Comment: 47 pages, 10 figures, 15 tables Submitted to Phys. Rev.
Advances in structure elucidation of small molecules using mass spectrometry
The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules