Estimating and comparing the rates of gene discovery and expressed sequence tag (EST) frequencies in EST surveys

MOTIVATION: Expressed sequence tag (EST) surveys are an efficient way to characterize large numbers of genes from an organism. The rate of gene discovery in an EST survey depends on the degree of redundancy of the cDNA libraries from which sequences are obtained. However, few statistical methods have been developed to assess and compare redundancies of various libraries from preliminary EST surveys. RESULTS: We consider statistics for the comparison of EST libraries based upon the frequencies with which genes occur in subsamples of reads. These measures are useful in determining which one of several libraries is more likely to yield new genes in future reads and what proportion of additional reads one might want to take from the libraries in order to be likely to obtain new genes. One approach is to compare single sample measures that have been successfully used in species estimation problems, such as coverage of a library, defined as the proportion of the library that is represented in the given sample of reads. Another single library measure is an estimate of the expected number of additional genes that will be found in a new sample of reads. We also propose statistics that jointly use data from all the libraries. Analogous formulas for coverage and the expected numbers of new genes are presented. These measures consider coverage in a single library based upon reads from all libraries and similarly, the expected numbers of new genes that will be discovered by taking reads from all libraries with fixed proportions. Together, the statistics presented provide useful comparative measures for the libraries that can be used to guide sampling from each of the libraries to maximize the rate of gene discovery. Finally, we present tests for whether genes are equally represented or expressed in a set of libraries. Binomial and chi2 tests are presented for gene-by-gene comparisons of expression. Overall tests of the equality of proportional representation are presented and multiple comparisons issues are addressed. These methods can be used to evaluate changes in gene expression reflected in the composition of EST libraries prepared from different tissue types or cells exposed to different environmental conditions. AVAILABILITY: Software will be made available at http://www.mathstat.dal.ca/~tsusko     

PathFinder: reconstruction and dynamic visualization of metabolic pathways

MOTIVATION: Beyond methods for a gene-wise annotation and analysis of sequenced genomes new automated methods for functional analysis on a higher level are needed. The identification of realized metabolic pathways provides valuable information on gene expression and regulation. Detection of incomplete pathways helps to improve a constantly evolving genome annotation or discover alternative biochemical pathways. To utilize automated genome analysis on the level of metabolic pathways new methods for the dynamic representation and visualization of pathways are needed. RESULTS: PathFinder is a tool for the dynamic visualization of metabolic pathways based on annotation data. Pathways are represented as directed acyclic graphs, graph layout algorithms accomplish the dynamic drawing and visualization of the metabolic maps. A more detailed analysis of the input data on the level of biochemical pathways helps to identify genes and detect improper parts of annotations. As an Relational Database Management System (RDBMS) based internet application PathFinder reads a list of EC-numbers or a given annotation in EMBL- or Genbank-format and dynamically generates pathway graphs.     

Inference of disease-related molecular logic from systems-based microarray analysis

Computational analysis of gene expression data from microarrays has been useful for medical diagnosis and prognosis. The ability to analyze such data at the level of biological modules, rather than individual genes, has been recognized as important for improving our understanding of disease-related pathways. It has proved difficult, however, to infer pathways from microarray data by deriving modules of multiple synergistically interrelated genes, rather than individual genes. Here we propose a systems-based approach called Entropy Minimization and Boolean Parsimony (EMBP) that identifies, directly from gene expression data, modules of genes that are jointly associated with disease. Furthermore, the technique provides insight into the underlying biomolecular logic by inferring a logic function connecting the joint expression levels in a gene module with the outcome of disease. Coupled with biological knowledge, this information can be useful for identifying disease-related pathways, suggesting potential therapeutic approaches for interfering with the functions of such pathways. We present an example providing such gene modules associated with prostate cancer from publicly available gene expression data, and we successfully validate the results on additional independently derived data. Our results indicate a link between prostate cancer and cellular damage from oxidative stress combined with inhibition of apoptotic mechanisms normally triggered by such damage.     

Metagenomic Data Utilization and Analysis (MEDUSA) and Construction of a Global Gut Microbial Gene Catalogue

Metagenomic sequencing has contributed important new knowledge about the microbes that live in a symbiotic relationship with humans. With modern sequencing technology it is possible to generate large numbers of sequencing reads from a metagenome but analysis of the data is challenging. Here we present the bioinformatics pipeline MEDUSA that facilitates analysis of metagenomic reads at the gene and taxonomic level. We also constructed a global human gut microbial gene catalogue by combining data from 4 studies spanning 3 continents. Using MEDUSA we mapped 782 gut metagenomes to the global gene catalogue and a catalogue of sequenced microbial species. Hereby we find that all studies share about half a million genes and that on average 300 000 genes are shared by half the studied subjects. The gene richness is higher in the European studies compared to Chinese and American and this is also reflected in the species richness. Even though it is possible to identify common species and a core set of genes, we find that there are large variations in abundance of species and genes.     

A statistical framework for eQTL mapping using RNA-seq data

RNA-seq may replace gene expression microarrays in the near future. Using RNA-seq, the expression of a gene can be estimated using the total number of sequence reads mapped to that gene, known as the total read count (TReC). Traditional expression quantitative trait locus (eQTL) mapping methods, such as linear regression, can be applied to TReC measurements after they are properly normalized. In this article, we show that eQTL mapping, by directly modeling TReC using discrete distributions, has higher statistical power than the two-step approach: data normalization followed by linear regression. In addition, RNA-seq provides information on allele-specific expression (ASE) that is not available from microarrays. By combining the information from TReC and ASE, we can computationally distinguish cis- and trans-eQTL and further improve the power of cis-eQTL mapping. Both simulation and real data studies confirm the improved power of our new methods. We also discuss the design issues of RNA-seq experiments. Specifically, we show that by combining TReC and ASE measurements, it is possible to minimize cost and retain the statistical power of cis-eQTL mapping by reducing sample size while increasing the number of sequence reads per sample. In addition to RNA-seq data, our method can also be employed to study the genetic basis of other types of sequencing data, such as chromatin immunoprecipitation followed by DNA sequencing data. In this article, we focus on eQTL mapping of a single gene using the association-based method. However, our method establishes a statistical framework for future developments of eQTL mapping methods using RNA-seq data (e.g., linkage-based eQTL mapping), and the joint study of multiple genetic markers and/or multiple genes.     

Match/X,A Gene Expression Pattern Recognition Algorithm Used to Identify Genes Which May be Related to CDC2 Function and Cell Cycle Regulation

Large-scale microarray gene expression studies can provide insight into complex genetic networks and biological pathways. A comprehensive gene expression database was constructed using Affymetrix GeneChip microarrays and RNA isolated from more than 6,400 distinct normal and diseased human tissues. These individual patient samples were grouped into over 700 sample sets based on common tissue and disease morphologies, and each set contained averaged expression data for over 45,000 gene probe sets representing more than 33,000 known human genes. Sample sets were compared to each other in more than 750 normal vs. disease pairwise comparisons. Relative up or down-regulation patterns of genes across these pairwise comparisons provided unique expression fingerprints that could be compared and matched to a gene of interest using the Match/X algorithm. This algorithm uses the kappa statistic to compute correlations between genes and calculate a distance score between a gene of interest and all other genes in the database. Using cdc2 as a query gene, we identified several hundred genes that had similar expression patterns and highly correlated distance scores. Most of these genes were known components of the cell cycle involved in G2/M progression, spindle function or chromosome arrangement. Some of the identified genes had unknown biological functions but may be related to cdc2 mediated mechanism based on their closely correlated distance scores. This algorithm may provide novel insights into unknown gene function based on correlation to expression profiles of known genes and can identify elements of cellular pathways and gene interactions in a high throughput fashion.     

A draft genome of field pennycress (Thlaspi arvense) provides tools for the domestication of a new winter biofuel crop

Field pennycress (Thlaspi arvense L.) is being domesticated as a new winter cover crop and biofuel species for the Midwestern United States that can be double-cropped between corn and soybeans. A genome sequence will enable the use of new technologies to make improvements in pennycress. To generate a draft genome, a hybrid sequencing approach was used to generate 47 Gb of DNA sequencing reads from both the Illumina and PacBio platforms. These reads were used to assemble 6,768 genomic scaffolds. The draft genome was annotated using the MAKER pipeline, which identified 27,390 predicted protein-coding genes, with almost all of these predicted peptides having significant sequence similarity to Arabidopsis proteins. A comprehensive analysis of pennycress gene homologues involved in glucosinolate biosynthesis, metabolism, and transport pathways revealed high sequence conservation compared with other Brassicaceae species, and helps validate the assembly of the pennycress gene space in this draft genome. Additional comparative genomic analyses indicate that the knowledge gained from years of basic Brassicaceae research will serve as a powerful tool for identifying gene targets whose manipulation can be predicted to result in improvements for pennycress.