A strand specific high resolution normalization method for chip-sequencing data employing multiple experimental control measurements

Background: High-throughput sequencing is becoming the standard tool for investigating protein-DNA interactions or epigenetic modifications. However, the data generated will always contain noise due to e. g. repetitive regions or non-specific antibody interactions. The noise will appear in the form of a background distribution of reads that must be taken into account in the downstream analysis, for example when detecting enriched regions (peak-calling). Several reported peak-callers can take experimental measurements of background tag distribution into account when analysing a data set. Unfortunately, the background is only used to adjust peak calling and not as a preprocessing step that aims at discerning the signal from the background noise. A normalization procedure that extracts the signal of interest would be of universal use when investigating genomic patterns. Results: We formulated such a normalization method based on linear regression and made a proof-of-concept implementation in R and C++. It was tested on simulated as well as on publicly available ChIP-seq data on binding sites for two transcription factors, MAX and FOXA1 and two control samples, Input and IgG. We applied three different peak-callers to (i) raw (un-normalized) data using statistical background models and (ii) raw data with control samples as background and (iii) normalized data without additional control samples as background. The fraction of called regions containing the expected transcription factor binding motif was largest for the normalized data and evaluation with qPCR data for FOXA1 suggested higher sensitivity and specificity using normalized data over raw data with experimental background. Conclusions: The proposed method can handle several control samples allowing for correction of multiple sources of bias simultaneously. Our evaluation on both synthetic and experimental data suggests that the method is successful in removing background noise

Bayesian detection of periodic mRNA time profiles without use of training examples

BACKGROUND: Detection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. Commonly the investigator is only interested in genes expressed at a particular frequency that characterizes the process under study but this frequency is seldom exactly known. Previously proposed detector designs require access to labelled training examples and do not allow systematic incorporation of diffuse prior knowledge available about the period time. RESULTS: A learning-free Bayesian detector that does not rely on labelled training examples and allows incorporation of prior knowledge about the period time is introduced. It is shown to outperform two recently proposed alternative learning-free detectors on simulated data generated with models that are different from the one used for detector design. Results from applying the detector to mRNA expression time profiles from S. cerevisiae showsthat the genes detected as periodically expressed only contain a small fraction of the cell-cycle genes inferred from mutant phenotype. For example, when the probability of false alarm was equal to 7%, only 12% of the cell-cycle genes were detected. The genes detected as periodically expressed were found to have a statistically significant overrepresentation of known cell-cycle regulated sequence motifs. One known sequence motif and 18 putative motifs, previously not associated with periodic expression, were also over represented. CONCLUSION: In comparison with recently proposed alternative learning-free detectors for periodic gene expression, Bayesian inference allows systematic incorporation of diffuse a priori knowledge about, e.g. the period time. This results in relative performance improvements due to increased robustness against errors in the underlying assumptions. Results from applying the detector to mRNA expression time profiles from S. cerevisiae include several new findings that deserve further experimental studies

Did Human Culture Emerge in a Cultural Evolutionary Transition in Individuality?

Evolutionary Transitions in Individuality (ETI) have been responsible for the major transitions in levels of selection and individuality in natural history, such as the origins of prokaryotic and eukaryotic cells, multicellular organisms, and eusocial insects.\ua0The integrated hierarchical organization of life thereby emerged as groups of individuals repeatedly evolved into new and more complex kinds of individuals. The Social Protocell Hypothesis (SPH) proposes that the integrated hierarchical organization of human culture can also be understood as the outcome of an ETI—one that produced a\ua0“cultural organism” (a “sociont”) from a substrate of socially learned traditions that were contained in growing and dividing social communities. The SPH predicts that a threshold\ua0degree of evolutionary individuality would have been achieved by 2.0–2.5 Mya, followed by an increasing degree of evolutionary individuality as the ETI unfolded. We here assess the SPH by applying a battery of criteria—developed to assess evolutionary individuality in biological units—to cultural units across the evolutionary history of Homo. We find an increasing agreement with these criteria, which buttresses the claim that an ETI occurred in the cultural realm

Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors

<p>Abstract</p> <p>Background</p> <p>We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process.</p> <p>Results</p> <p>We explain the regulatory mechanisms of the inferred periodic classes with <it>cis</it>-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of <it>cis</it>-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach.</p> <p>Conclusion</p> <p>The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.</p

The urban economy as a scale-free network

We present empirical evidence that land values are scale-free and introduce a network model that reproduces the observations. The network approach to urban modelling is based on the assumption that the market dynamics that generates land values can be represented as a growing scale-free network. Our results suggest that the network properties of trade between specialized activities causes land values, and likely also other observables such as population, to be power law distributed. In addition to being an attractive avenue for further analytical inquiry, the network representation is also applicable to empirical data and is thereby attractive for predictive modelling.Comment: Submitted to Phys. Rev. E. 7 pages, 3 figures. (Minor typos and details fixed

Asymptotic silence-breaking singularities

We discuss three complementary aspects of scalar curvature singularities: asymptotic causal properties, asymptotic Ricci and Weyl curvature, and asymptotic spatial properties. We divide scalar curvature singularities into two classes: so-called asymptotically silent singularities and non-generic singularities that break asymptotic silence. The emphasis in this paper is on the latter class which have not been previously discussed. We illustrate the above aspects and concepts by describing the singularities of a number of representative explicit perfect fluid solutions.Comment: 25 pages, 6 figure

Generation, characterization, and medical utilization of laser-produced emission continua

Intense continua of electromagnetic radiation of very brief duration are formed in the interaction of focused ultra-short terawatt laser pulses with matter. Two different kinds of experiments, which have been performed utilizing the Lund 10 Hz titanium-doped sapphire terawatt laser system are being described, where visible radiation and X-rays, respectively, have been generated. Focusing into water leads to the generation of a light continuum through self-phase modulation. The propagation of the light through tissue was studied addressing questions related to optical mammography and specific chromophore absorption. When terawatt laser pulses are focused onto a solid target with high nuclear charge Z, intense X-ray radiation of few ps duration and with energies exceeding hundreds of keV is emitted. Biomedical applications of this radiation are described, including differential absorption and gated-viewing imaging

SICTIN: Rapid footprinting of massively parallel sequencing data

BACKGROUND: Massively parallel sequencing allows for genome-wide hypothesis-free investigation of for instance transcription factor binding sites or histone modifications. Although nucleotide resolution detailed information can easily be generated, biological insight often requires a more general view of patterns (footprints) over distinct genomic features such as transcription start sites, exons or repetitive regions. The construction of these footprints is however a time consuming task. METHODS: The presented software generates a binary representation of the signals enabling fast and scalable lookup. This representation allows for footprint generation in mere minutes on a desktop computer. Several different input formats are accepted, e.g. the SAM format, bed-files and the UCSC wiggle track. CONCLUSIONS: Hypothesis-free investigation of genome wide interactions allows for biological data mining at a scale never before seen. Until recently, the main focus of analysis of sequencing data has been targeted on signal patterns around transcriptional start sites which are in manageable numbers. Today, focus is shifting to a wider perspective and numerous genomic features are being studied. To this end, we provide a system allowing for fast querying in the order of hundreds of thousands of features

Evidence of Color Coherence Effects in W+jets Events from ppbar Collisions at sqrt(s) = 1.8 TeV

We report the results of a study of color coherence effects in ppbar collisions based on data collected by the D0 detector during the 1994-1995 run of the Fermilab Tevatron Collider, at a center of mass energy sqrt(s) = 1.8 TeV. Initial-to-final state color interference effects are studied by examining particle distribution patterns in events with a W boson and at least one jet. The data are compared to Monte Carlo simulations with different color coherence implementations and to an analytic modified-leading-logarithm perturbative calculation based on the local parton-hadron duality hypothesis.Comment: 13 pages, 6 figures. Submitted to Physics Letters