Background Genetic screens in Drosophila have provided a wealth of information about a variety of cellular and developmental processes. It is now possible to screen for mutant phenotypes in virtually any cell at any stage of development by performing clonal screens using the flp/FRT system. The rate-limiting step in the analysis of these mutants is often the identification of the mutated gene, however, because traditional mapping strategies rely mainly on genetic and cytological markers that are not easily linked to the molecular map. Results Here we describe the development of a single-nucleotide polymorphism (SNP) map for chromosome arm 3R. The map contains 73 polymorphisms between the standard FRT chromosome, and a mapping chromosome that carries several visible markers (rucuca), at an average density of one SNP per 370 kilobases (kb). Using this collection, we show that mutants can be mapped to a 400 kb interval in a single meiotic mapping cross, with only a few hundred SNP detection reactions. Discovery of further SNPs in the region of interest allows the mutation to be mapped with the same recombinants to a region of about 50 kb. Conclusion The combined use of standard visible markers and molecular polymorphisms in a single mapping strategy greatly reduces both the time and cost of mapping mutations, because it requires at least four times fewer SNP detection reactions than a standard approach. The use of this map, or others developed along the same lines, will greatly facilitate the identification of the molecular lesions in mutants from clonal screens.

Background cDNA-RDA is one of the subtractive cloning techniques used to isolate differentially expressed genes between two complex cDNA populations. In the present study we present a modification of the protocol described by Hubank and Schatz. Results In the post-hybridization mix, the 5'-ends of homoduplexes of interest (tester-tester) are filled-in with α-thio-deoxynucleotides. Unprotected duplexes, as well as the single-stranded DNA fragments, are degraded using ExoIII and Mung Bean Nuclease, prior to PCR subtraction, resulting in less complex difference products. We illustrate this modification by the cloning of a new gene which is differentially expressed in normal, reelin and Dab1 mutant mice and is a candidate member of the Reelin signalling pathway involved in brain development. Conclusion We propose a modification of cDNA-RDA that may reduce the complexity of the post-hybridization mix and thus facilitate the amplification of differentially expressed products.

Background We performed a statistical analysis of a previously published set of gene expression microarray data from six different brain regions in two mouse strains. In the previous analysis, 24 genes showing expression differences between the strains and about 240 genes with regional differences in expression were identified. Like many gene expression studies, that analysis relied primarily on ad hoc 'fold change' and 'absent/present' criteria to select genes. To determine whether statistically motivated methods would give a more sensitive and selective analysis of gene expression patterns in the brain, we decided to use analysis of variance (ANOVA) and feature selection methods designed to select genes showing strain- or region-dependent patterns of expression. Results Our analysis revealed many additional genes that might be involved in behavioral differences between the two mouse strains and functional differences between the six brain regions. Using conservative statistical criteria, we identified at least 63 genes showing strain variation and approximately 600 genes showing regional variation. Unlike ad hoc methods, ours have the additional benefit of ranking the genes by statistical score, permitting further analysis to focus on the most significant. Comparison of our results to the previous studies and to published reports on individual genes show that we achieved high sensitivity while preserving selectivity. Conclusions Our results indicate that molecular differences between the strains and regions studied are larger than indicated previously. We conclude that for large complex datasets, ANOVA and feature selection, alone or in combination, are more powerful than methods based on fold-change thresholds and other ad hoc selection criteria.

A combination of linear RNA amplification and DNA microarray hybridization has allowed the determination of expression profiles of individual imaginal discs and larval tissues and the identification of genes expressed in tissue-specific patterns.

Background Transcriptional regulation in eukaryotes generally operates at the level of individual genes. Regulation of sets of adjacent genes by mechanisms operating at the level of chromosomal domains has been demonstrated in a number of cases, but the fraction of genes in the genome subject to regulation at this level is unknown. Results Drosophila gene-expression profiles that were determined from over 80 experimental conditions using high-density oligonucleotide microarrays were searched for groups of adjacent genes that show similar expression profiles. We found about 200 groups of adjacent and similarly expressed genes, each having between 10 and 30 members; together these groups account for over 20% of assayed genes. Each group covers between 20 and 200 kilobase pairs of genomic sequence, with a mean group size of about 100 kilobase pairs. Groups do not appear to show any correlation with polytene banding patterns or other known chromosomal structures, nor were genes within groups functionally related to one another. Conclusions Groups of adjacent and co-regulated genes that are not otherwise functionally related in any obvious way can be identified by expression profiling in Drosophila. The mechanism underlying this phenomenon is not yet known.

Background Sites in DNA that bind regulatory proteins can be detected computationally in various ways. Pattern discovery methods analyze collections of genes suspected to be co-regulated on the evidence, for example, of clustering of transcriptome data. Pattern searching methods use sequences with known binding sites to find other genes regulated by a given protein. Such computational methods are important strategies in the discovery and elaboration of regulatory networks and can provide the experimental biologist with a precise prediction of a binding site or identify a gene as a member of a set of co-regulated genes (a regulon). As more variations on such methods are published, however, thorough evaluation is necessary, as performance may differ depending on the conditions of use. Detailed evaluation also helps to improve and understand the behavior of the different methods and computational strategies. Results We used a collection of 86 regulons from Escherichia coli as datasets to evaluate two methods for pattern discovery and pattern searching: dyad analysis/dyad sweeping using the program Dyad-analysis, and multiple alignment using the programs Consensus/Patser. Clearly defined statistical parameters are used to evaluate the two methods in different situations. We placed particular emphasis on minimizing the rate of false positives. Conclusions As a general rule, sensors obtained from experimentally reported binding sites in DNA frequently locate true sites as the highest-scoring sequences within a given upstream region, especially using Consensus/Patser. Pattern discovery is still an unsolved problem, although in the cases where Dyad-analysis finds significant dyads (around 50%), these frequently correspond to true binding sites. With more robust methods, regulatory predictions could help identify the function of unknown genes.

Fold-change' cutoffs have been widely used in microarray assays to identify genes that are differentially expressed. More accurate measures are required to identify high-confidence sets of genes with biologically meaningful changes in transcription. A general procedure for analyzing cDNA microarray data is proposed and validated. It is shown that pooled reference samples should be based not only on the expression of individual genes in each cell line but also on the expression levels of genes within cell lines.

Background Gene expression profiling among different tissues is of paramount interest in various areas of biomedical research. We have developed a novel method (DADA, Digital Analysis of cDNA Abundance), that calculates the relative abundance of genes in cDNA libraries. Results DADA is based upon multiple restriction fragment length analysis of pools of clones from cDNA libraries and the identification of gene-specific restriction fingerprints in the resulting complex fragment mixtures. A specific cDNA cloning vector had to be constructed that governed missing or incomplete cDNA inserts which would generate misleading fingerprints in standard cloning vectors. Double stranded cDNA was synthesized using an anchored oligo dT primer, uni-directionally inserted into the DADA vector and cDNA libraries were constructed in E. coli. The cDNA fingerprints were generated in a PCR-free procedure that allows for parallel plasmid preparation, labeling, restriction digest and fragment separation of pools of 96 colonies each. This multiplexing significantly enhanced the throughput in comparison to sequence-based methods (e.g. EST approach). The data of the fragment mixtures were integrated into a relational database system and queried with fingerprints experimentally produced by analyzing single colonies. Due to limited predictability of the position of DNA fragments on the polyacrylamid gels of a given size, fingerprints derived solely from cDNA sequences were not accurate enough to be used for the analysis. We applied DADA to the analysis of gene expression profiles in a model for impaired wound healing (treatment of mice with dexamethasone). Conclusions The method proved to be capable of identifying pharmacologically relevant target genes that had not been identified by other standard methods routinely used to find differentially expressed genes. Due to the above mentioned limited predictability of the fingerprints, the method was yet tested only with a limited number of experimentally determined fingerprints and was able to detect differences in gene expression of transcripts representing 0.05% of the total mRNA population (e.g. medium abundant gene transcripts).

This file contains the data on apoptosis, neurite outgrowth, synaptogenesis and proliferation. This dataset is associated with the following publication: Harrill, J., T. Freudenrich, K. Wallace, K. Ball, T. Shafer, and W. Mundy. Testing for developmental neurotoxicity using a battery of in vitro assays for key cellular events in neurodevelopment. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 354(1): 24-39, (2018).

This file contains the data on apoptosis, neurite outgrowth, synaptogenesis and proliferation. This dataset is associated with the following publication: Harrill, J., T. Freudenrich, K. Wallace, K. Ball, T. Shafer, and W. Mundy. Testing for developmental neurotoxicity using a battery of in vitro assays for key cellular events in neurodevelopment. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 354(1): 24-39, (2018).