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

Modified method for the extraction of mRNA from paddy soils

OBJECTIVE

Paddy soils contain relatively greater organic matter and water contents than other soils thereby limiting effective mRNA extraction. A modification of the conventional mRNA soil extraction method specific to paddy soils is described.

RESULTS

Two main modifications for co-extraction of DNA and RNA are: (1) addition of 20 % (w/v) sodium dodecyl sulphate to 10 % (w/v) hexadecyltrimethylammonium bromide extraction buffer, and (2) fresh soil, initially frozen at -80 °C, is immediately immersed in extraction buffer. The high-quality total RNA extracted can be directly used in downstream analyses without an additional step to remove humic acid. RNA purification was conducted to remove 5S rRNA, and the mRNA was enriched by selectively digesting rRNA. cDNA synthesised by reverse transcriptase was not contaminated by the reagents or genomic DNA.

CONCLUSION

The modified method for mRNA extraction from paddy soil is suitable for analysing the expression of microbial genes from fresh paddy soil.

Extraction of mRNA from soil

Here, we report an efficient method for extracting high-quality mRNA from soil. Key steps in the isolation of total RNA were low-pH extraction (pH 5.0) and Q-Sepharose chromatography. The removal efficiency of humic acids was 94 to 98% for all soils tested. To enrich mRNA, subtractive hybridization of rRNA was most efficient. Subtractive hybridization may be followed by exonuclease treatment if the focus is on the analysis of unprocessed mRNA. The total extraction method can be completed within 8 h, resulting in enriched mRNA ranging from 200 bp to 4 kb in size.

Bayesian detection of non-sinusoidal periodic patterns in circadian expression data

MOTIVATION

Cyclical biological processes such as cell division and circadian regulation produce coordinated periodic expression of thousands of genes. Identification of such genes and their expression patterns is a crucial step in discovering underlying regulatory mechanisms. Existing computational methods are biased toward discovering genes that follow sine-wave patterns.

RESULTS

We present an analysis of variance (ANOVA) periodicity detector and its Bayesian extension that can be used to discover periodic transcripts of arbitrary shapes from replicated gene expression profiles. The models are applicable when the profiles are collected at comparable time points for at least two cycles. We provide an empirical Bayes procedure for estimating parameters of the prior distributions and derive closed-form expressions for the posterior probability of periodicity, enabling efficient computation. The model is applied to two datasets profiling circadian regulation in murine liver and skeletal muscle, revealing a substantial number of previously undetected non-sinusoidal periodic transcripts in each. We also apply quantitative real-time PCR to several highly ranked non-sinusoidal transcripts in liver tissue found by the model, providing independent evidence of circadian regulation of these genes.

AVAILABILITY

Matlab software for estimating prior distributions and performing inference is available for download from http://www.datalab.uci.edu/resources/periodicity/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcriptional activity of paddy soil bacterial communities

Bulk mRNA was used to explore the transcriptional activity of bacterial communities in oxic versus anoxic paddy soil. Two microbial cDNA libraries were constructed from composite samples using semi-randomly primed RT-PCR. cDNAs averaged 500-600 bp in length and were treated as expressed sequence tags (ESTs). Clustering analysis of 805 random cDNAs resulted in 179 and 155 different ESTs for the oxic and anoxic zones respectively. Using an E-value threshold of e(-10), a total of 218 different ESTs could be assigned by blastx, while 116 ESTs were predicted novel. Both the proportion and significance of the EST assignments increased with cDNA length. Taxonomic assignment was more powerful in discriminating between the aerobic and anaerobic bacterial communities than functional inference, as most ESTs in both oxygen zones were putative indicators of similar housekeeping functions, in particular ABC-type transporters. A few ESTs were putative indicators for community function in a biogeochemical context, such as beta-oxidation of long-chain fatty acids specifically in the oxic zone. Expressed sequence tags assigned to Alpha- and Betaproteobacteria were predominantly found in the oxic zone, while those affiliated with Deltaproteobacteria were more frequently detected in the anoxic zone. At the genus level, multiple assignments to Bradyrhizobium and Geobacter were unique to the oxic and anoxic zones respectively. The phylum-level affiliations of 93 16S rRNA sequences corresponded well with two taxonomically distinct EST patterns. Expressed sequence tags affiliated with Acidobacteria and Chloroflexi were frequently detected in both oxygen zones. In summary, the soil metatranscriptome is accessible for global analysis and such studies have great potential in elucidating the taxonomic and functional status of soil bacterial communities, but study significance depends on the number and length of cDNAs being randomly analysed.

Towards knowledge-based gene expression data mining

The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the plethora of gene expression data mining techniques and focus on their evolution toward knowledge-based data analysis approaches. In particular, we discuss recent developments in gene expression-based analysis methods used in association and classification studies, phenotyping and reverse engineering of gene networks.

Cyclebase.org--a comprehensive multi-organism online database of cell-cycle experiments

The past decade has seen the publication of a large number of cell-cycle microarray studies and many more are in the pipeline. However, data from these experiments are not easy to access, combine and evaluate. We have developed a centralized database with an easy-to-use interface, Cyclebase.org, for viewing and downloading these data. The user interface facilitates searches for genes of interest as well as downloads of genome-wide results. Individual genes are displayed with graphs of expression profiles throughout the cell cycle from all available experiments. These expression profiles are normalized to a common timescale to enable inspection of the combined experimental evidence. Furthermore, state-of-the-art computational analyses provide key information on both individual experiments and combined datasets such as whether or not a gene is periodically expressed and, if so, the time of peak expression. Cyclebase is available at http://www.cyclebase.org.

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

Background

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.

Results

We explain the regulatory mechanisms of the inferred periodic classes with cis-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 cis-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.

Conclusion

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.

ProbCD: enrichment analysis accounting for categorization uncertainty

Background

As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or enhancement) analysis. In spite of efforts to create probabilistic annotations, especially in the Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment methods largely ignore this probabilistic information since they are mainly based on variants of the Fisher Exact Test.

Results

We developed an open-source R-based software to deal with probabilistic categorical data analysis, ProbCD, that does not require a static contingency table. The contingency table for the enrichment problem is built using the expectation of a Bernoulli Scheme stochastic process given the categorization probabilities. An on-line interface was created to allow usage by non-programmers and is available at: .

Conclusion

We present an analysis framework and software tools to address the issue of uncertainty in categorical data analysis. In particular, concerning the enrichment analysis, ProbCD can accommodate: (i) the stochastic nature of the high-throughput experimental techniques and (ii) probabilistic gene annotation.

Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data

Background

In practice many biological time series measurements, including gene microarrays, are conducted at time points that seem to be interesting in the biologist's opinion and not necessarily at fixed time intervals. In many circumstances we are interested in finding targets that are expressed periodically. To tackle the problems of uneven sampling and unknown type of noise in periodicity detection, we propose to use robust regression.

Methods

The aim of this paper is to develop a general framework for robust periodicity detection and review and rank different approaches by means of simulations. We also show the results for some real measurement data.

Results

The simulation results clearly show that when the sampling of time series gets more and more uneven, the methods that assume even sampling become unusable. We find that M-estimation provides a good compromise between robustness and computational efficiency.

Conclusion

Since uneven sampling occurs often in biological measurements, the robust methods developed in this paper are expected to have many uses. The regression based formulation of the periodicity detection problem easily adapts to non-uniform sampling. Using robust regression helps to reject inconsistently behaving data points.

Availability

The implementations are currently available for Matlab and will be made available for the users of R as well. More information can be found in the web-supplement [1].

Spectral estimation in unevenly sampled space of periodically expressed microarray time series data

BACKGROUND

Periodogram analysis of time-series is widespread in biology. A new challenge for analyzing the microarray time series data is to identify genes that are periodically expressed. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, and unevenly sampled time points. Most methods used in the literature operate on evenly sampled time series and are not suitable for unevenly sampled time series.

RESULTS

For evenly sampled data, methods based on the classical Fourier periodogram are often used to detect periodically expressed gene. Recently, the Lomb-Scargle algorithm has been applied to unevenly sampled gene expression data for spectral estimation. However, since the Lomb-Scargle method assumes that there is a single stationary sinusoid wave with infinite support, it introduces spurious periodic components in the periodogram for data with a finite length. In this paper, we propose a new spectral estimation algorithm for unevenly sampled gene expression data. The new method is based on signal reconstruction in a shift-invariant signal space, where a direct spectral estimation procedure is developed using the B-spline basis. Experiments on simulated noisy gene expression profiles show that our algorithm is superior to the Lomb-Scargle algorithm and the classical Fourier periodogram based method in detecting periodically expressed genes. We have applied our algorithm to the Plasmodium falciparum and Yeast gene expression data and the results show that the algorithm is able to detect biologically meaningful periodically expressed genes.

CONCLUSION

We have proposed an effective method for identifying periodic genes in unevenly sampled space of microarray time series gene expression data. The method can also be used as an effective tool for gene expression time series interpolation or resampling.