Discarding duplicate ditags in LongSAGE analysis may introduce significant error

Background  

During gene expression analysis by Serial Analysis of Gene Expression (SAGE), duplicate ditags are routinely removed from the data analysis, because they are suspected to stem from artifacts during SAGE library construction. As a consequence, naturally occurring duplicate ditags are also removed from the analysis leading to an error of measurement.     

A seriation approach for visualization-driven discovery of co-expression patterns in Serial Analysis of Gene Expression (SAGE) data

Background

Serial Analysis of Gene Expression (SAGE) is a DNA sequencing-based method for large-scale gene expression profiling that provides an alternative to microarray analysis. Most analyses of SAGE data aimed at identifying co-expressed genes have been accomplished using various versions of clustering approaches that often result in a number of false positives.

Principal Findings

Here we explore the use of seriation, a statistical approach for ordering sets of objects based on their similarity, for large-scale expression pattern discovery in SAGE data. For this specific task we implement a seriation heuristic we term ‘progressive construction of contigs’ that constructs local chains of related elements by sequentially rearranging margins of the correlation matrix. We apply the heuristic to the analysis of simulated and experimental SAGE data and compare our results to those obtained with a clustering algorithm developed specifically for SAGE data. We show using simulations that the performance of seriation compares favorably to that of the clustering algorithm on noisy SAGE data.

Conclusions

We explore the use of a seriation approach for visualization-based pattern discovery in SAGE data. Using both simulations and experimental data, we demonstrate that seriation is able to identify groups of co-expressed genes more accurately than a clustering algorithm developed specifically for SAGE data. Our results suggest that seriation is a useful method for the analysis of gene expression data whose applicability should be further pursued.     

Generation of high-quantity and quality tag/ditag cDNAs for SAGE analysis

The serial analysis of gene expression (SAGE) technique is an important tool for genome-wide gene expression analysis. However, the requirement of a large amount of mRNA for the analysis and the difficulties in generating high-quality tag and ditag fragments for the construction of a SAGE library often interfere with the successful performance of the SAGE technique. We developed two procedures to solve these issues: (i) introducing low-cycle PCR amplification of the 3' cDNA before the BsmFI digestion of the 3' cDNAs and (ii) gel purifying the BsmFI-released tag fragments before ditag formation. These modifications provide a large quantity of initial 3' cDNAs and high-quality tags and ditags for the construction of SAGE libraries.     

Accurate and unambiguous tag-to-gene mapping in serial analysis of gene expression

Background  

In this study, we present a robust and reliable computational method for tag-to-gene assignment in serial analysis of gene expression (SAGE). The method relies on current genome information and annotation, incorporation of several new features, and key improvements over alternative methods, all of which are important to determine gene expression levels more accurately. The method provides a complete annotation of potential virtual SAGE tags within a genome, along with an estimation of their confidence for experimental observation that ranks tags that present multiple matches in the genome.     

Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays

Background  

The Audic-Claverie method [1] has been and still continues to be a popular approach for detection of differentially expressed genes in the SAGE framework. The method is based on the assumption that under the null hypothesis tag counts of the same gene in two libraries come from the same but unknown Poisson distribution. The problem is that each SAGE library represents only a single measurement. We ask: Given that the tag count samples from SAGE libraries are extremely limited, how useful actually is the Audic-Claverie methodology? We rigorously analyze the A-C statistic that forms a backbone of the methodology and represents our knowledge of the underlying tag generating process based on one observation.     

Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates

Background  

Two major identifiable sources of variation in data derived from the Serial Analysis of Gene Expression (SAGE) are within-library sampling variability and between-library heterogeneity within a group. Most published methods for identifying differential expression focus on just the sampling variability. In recent work, the problem of assessing differential expression between two groups of SAGE libraries has been addressed by introducing a beta-binomial hierarchical model that explicitly deals with both of the above sources of variation. This model leads to a test statistic analogous to a weighted two-sample t-test. When the number of groups involved is more than two, however, a more general approach is needed.     

Identifying differential expression in multiple SAGE libraries: an overdispersed log-linear model approach

Background  

In testing for differential gene expression involving multiple serial analysis of gene expression (SAGE) libraries, it is critical to account for both between and within library variation. Several methods have been proposed, including the t test, t _w test, and an overdispersed logistic regression approach. The merits of these tests, however, have not been fully evaluated. Questions still remain on whether further improvements can be made.     

High-performance liquid chromatography purification of 26-bp serial analysis of gene expression ditags results in higher yields,longer concatemers,and substantial time savings

In contrast to DNA chips, serial analysis of gene expression (SAGE) is not dependent on genes having been previously identified for their monitoring. Although useful, the method can be technically challenging, and particularly the last steps including concatenation and cloning may result in less than optimal results. We propose that many of the encountered problems can be attributed to the purification of the 26-bp ditags by polyacrylamide gel electrophoresis. Low yields, gel contaminants, potential exposure to degrading enzymes during handling and lengthy separation all disfavor the method. We introduce purification of 26-bp ditags by reverse-phase high-performance liquid chromatography (HPLC) using polystyrene/divinylbenzene columns and tetraethylammonium acetate buffer with acetonitrile as mobile phase. The method is fast and gives excellent results. Ditags purified by HPLC readily ligate to high-molecular-weight concatemers leading to their efficient cloning. The method should substantially facilitate the construction of SAGE libraries.     

SAGE: String-overlap Assembly of GEnomes

Background

De novo genome assembly of next-generation sequencing data is one of the most important current problems in bioinformatics, essential in many biological applications. In spite of significant amount of work in this area, better solutions are still very much needed.

Results

We present a new program, SAGE, for de novo genome assembly. As opposed to most assemblers, which are de Bruijn graph based, SAGE uses the string-overlap graph. SAGE builds upon great existing work on string-overlap graph and maximum likelihood assembly, bringing an important number of new ideas, such as the efficient computation of the transitive reduction of the string overlap graph, the use of (generalized) edge multiplicity statistics for more accurate estimation of read copy counts, and the improved use of mate pairs and min-cost flow for supporting edge merging. The assemblies produced by SAGE for several short and medium-size genomes compared favourably with those of existing leading assemblers.

Conclusions

SAGE benefits from innovations in almost every aspect of the assembly process: error correction of input reads, string-overlap graph construction, read copy counts estimation, overlap graph analysis and reduction, contig extraction, and scaffolding. We hope that these new ideas will help advance the current state-of-the-art in an essential area of research in genomics.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-302) contains supplementary material, which is available to authorized users.