Sex-specific expression of alternative transcripts in Drosophila
© McIntyre et al.; licensee BioMed Central Ltd. 2006
Received: 15 February 2006
Accepted: 25 August 2006
Published: 25 August 2006
Many genes produce multiple transcripts due to alternative splicing or utilization of alternative transcription initiation/termination sites. This 'transcriptome expansion' is thought to increase phenotypic complexity by allowing a single locus to produce several functionally distinct proteins. However, sex, genetic and developmental variation in the representation of alternative transcripts has never been examined systematically. Here, we describe a genome-wide analysis of sex-specific expression of alternative transcripts in Drosophila melanogaster.
We compared transcript profiles in males and females from eight Drosophila lines (OregonR and 2b, and 6 RIL) using a newly designed 60-mer oligonucleotide microarray that allows us to distinguish a large proportion of alternative transcripts. The new microarray incorporates 7,207 oligonucleotides, satisfying stringent binding and specificity criteria that target both the common and the unique regions of 2,768 multi-transcript genes, as well as 12,912 oligonucleotides that target genes with a single known transcript. We estimate that up to 22% of genes that produce multiple transcripts show a sex-specific bias in the representation of alternative transcripts. Sexual dimorphism in overall transcript abundance was evident for 53% of genes. The X chromosome contains a significantly higher proportion of genes with female-biased transcription than the autosomes. However, genes on the X chromosome are no more likely to have a sexual bias in alternative transcript representation than autosomal genes.
Widespread sex-specific expression of alternative transcripts in Drosophila suggests that a new level of sexual dimorphism at the molecular level exists.
Microarray hybridization, with its unprecedented ability to monitor genome-wide gene expression profiles, is paving the way for exploring previously intractable problems in developmental biology [1–5], neurobiology and behavior [6–8], evolutionary genetics [9–13], and other areas of biology. One of the technology's most exciting applications lies in establishing an experimental and theoretical framework for linking genetic variation in transcript abundance and phenotypic traits [14–19]. However, there is more to the regulation of gene expression than steady-state transcript abundance. In particular, many multi-exon genes in eukaryotic genomes are subject to alternative splicing, which is thought to increase phenotypic complexity by producing multiple, functionally distinct proteins [20–24]. Much of this alternative splicing may be tissue-specific, introducing an additional layer of regulatory complexity [22, 25]. Sexual dimorphism and genetic variation in alternative splicing have never been systematically examined, but it is reasonable to expect that such variation would have a considerable impact on phenotypic diversity.
To estimate the extent of sexual dimorphism and genetic variation in the production of alternative transcripts, we designed a new Drosophila whole-genome microarray that allows us to distinguish multiple transcripts of many genes using long (60-mer) oligonucleotide probes. Since genome annotation changes frequently as more data become available, we have created a flexible, easily updated design, and developed software that allows automatic annotation updates. We have used the new platform to compare gene expression profiles of males and females in eight lines of Drosophila melanogaster, and found that over 50% of all genes are expressed in a sex-biased manner. Interestingly, we estimate that between 11% and 24% of Drosophila genes known to produce multiple transcripts show sexual bias in the expression of alternative transcripts.
Results from ANOVA models for single and multiple transcripts for the set of 10,933 detected genes
Multiple transcript model
Single transcript model
Number of genes
Number significant for treatment
Number significant for line
Number significant for sex
For 828 of the 2,479 genes known to produce multiple transcripts, microarray probes targeting 2 or more distinct sets of transcripts showed detectable hybridization. These probes were analyzed using the ANOVA model for multiple transcripts. Expression levels of 653 (78%) of these genes showed significant variation at the FDR of 0.05, with the majority (544) showing a sex bias and 202 showing significant differences among lines (that is, genetic variation). For 91 gene families, hybridization was detected for probes targeting two or more sets of transcripts. Of these, 79 were variable, with 67 of these showing significant differences between males and females. For one transcript (modulo), the direction of the difference between males and females was affected by genotype.
Validation of platform
Probe targets and effects of sex and sex by probe interaction for several components of the sex determination pathway
-RA -RB -RD -RF -RG
-RA -RB -RF -RG
-RA -RB -RD -RF -RG
-RA -RB -RD -RF -RG
-RA -RC -RE -RG
-RB -RF -RK
-RD -RH -RJ
-RA -RC -RE -RG
-RB -RF -RK
-RD -RH -RJ
-RI -RJ -RL -RM
-RI -RJ -RL -RM
-RI -RJ -RL -RM
-RB -RE -RG
-RC -RF -RH
-RA -RD -RI -RJ -RK -RL -RM
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Next, we retrieved from FlyBase a list of genes known to be involved in the development or function of reproductive organs. We subdivided this list into three non-overlapping sets: genes known to function only in the female reproductive system (565 microarray probes, representing 326 genes), those known to function only in the male reproductive system (60 probes/42 genes), and genes implicated in both male and female reproductive systems (120 probes/86 genes). Most of these genes, however, are not exclusive to the reproductive system and are expressed in a wide range of non-reproductive organs as well. Since our experiments utilized whole-body RNA samples, we may not always be able to detect sex-biased expression in the reproductive organs. We found that among the female reproductive system genes, 86% were female-biased, with 72.5% being significant for sex and/or sex-by-probe interaction effect (Additional file 1). Conversely, among the male reproductive system genes, 64.3% were male-biased, with 55.5% showing significant sex effect (Additional file 1). We also analyzed a set of genes that are thought to be expressed only in males. These genes included a number of secreted accessory gland proteins [29–31], putative odorant-binding proteins expressed in male-specific chemosensory organs , and sperm-specific structural proteins . We found that 100% of these genes (11 out of 11) showed male-biased expression in our experiments (Additional file 1). Finally, we examined a set of male-specific transcripts identified earlier by differential cDNA hybridization [33, 34], and found that all genes detected in our experiments (ten out of ten) showed male-biased expression (Additional file 1). Finally, we examined the expression of six Y-linked genes represented on our arrays. Only two of them were expressed at detectable levels in enough samples to be considered informative. As expected, neither was present in any female samples, but both were detected in the majority of male samples. Together, these analyses confirm that the new microarray platform is effective for detecting sex-biased gene expression. For genes that produce multiple transcripts due to alternative splicing, or due to the presence of multiple transcription initiation or termination sites, we tested whether the relative proportions of alternative transcripts differed between sexes or lines. We used the ANOVA model for multiple transcripts (see Materials and methods) to examine the genes for which at least two probes targeting distinct sets of transcripts produced detectable hybridization. For these genes, we tested whether the relative amounts of signal from the different probes differed between sexes or lines. Such differences (called sex-by-probe or line-by-probe interactions) imply that the same gene produces alternative transcripts in different amounts in males versus females, or in different genotypes, respectively.
Sex-specific production of alternative transcripts has previously been reported for only a handful of genes, so we lack an extensive set of positive controls against which to compare our results. The best-known example in Drosophila is the dsx gene . Indeed, as shown above, probes targeting the male- and female-specific exons of dsx show different expression levels in different sexes (Table 2). When analyzed using the ANOVA model for multiple transcripts, the dsx gene shows a significant sex-by-probe interaction (P < 0.0001; Table 2). Sex-lethal (Sxl), which also produces male- and female-specific alternative transcripts , was represented in our experiments by five probes targeting different subsets of transcripts, and also showed significant sex-by-probe interaction (Table 2). These results suggest that our platform has the power to detect quantitative differences in the relative amount of alternative transcripts in different sexes.
Sex-specific expression of alternative transcripts
Genes with probes targeting two or more non-identical sets of transcripts expressed
Classification of probes
Number of genes total (alternatively transcribed)
Significant total (alternatively transcribed)
Local probes only
Global + 1 local probe
Global + 2 local probes
Global + 3 local probes
Global + 4 local probes
Global + 5 local probes
Global + 6 local probes
Global + 7 local probes
We did observe some exceptions where different probes targeting the same set of annotated transcripts showed different male/female expression ratios (Additional file 5). Such exceptions could be due either to intrinsic biases in probe hybridization, or to mistakes in the current FlyBase annotation (that is, exons indicated as constitutive might in fact be subject to alternative splicing or transcription). To estimate the extent to which our results may be affected by these factors, we used the ANOVA model for multiple transcripts to compare probes that, according to the current annotation, targeted different regions of the same set of transcripts. This control allows us to assess the maximum proportion of significant sex-by-probe or line-by-probe interactions expected in the absence of differential transcript production (see Materials and methods). Of the 1,321 control probe sets, 129 (9.77%) showed significant interactions - a proportion that is well short of the 22% found for probes targeting distinct sets of transcripts. This suggests that although intrinsic probe biases and/or mistakes in the annotation may have an effect, this effect is not sufficient to explain the observed variation in relative transcript abundance. We conclude that a large proportion of multi-transcript genes in the Drosophila genome produce alternative transcripts in a sexually dimorphic manner.
Confirmation of sex-specific alternative splicing by quantitative PCR
Genomic distribution of differentially expressed genes
We tested whether the genes that showed evidence of differences in gene expression were more likely to be located on the X chromosome than on the autosomes using a χ2 test. For single-transcript genes, 57% (840) of the X-linked genes showed a significant difference in gene expression among sexes or lines, compared to 54% (4,630) for the autosomal genes. This difference, while slight, is greater than expected by chance (P = 0.0260). In other words, X-linked genes are significantly more likely to show differences in gene expression than autosomal genes. We then tested whether male- and female-biased genes were distributed in the same proportions between the X chromosome and the autosomes. We identified 559 female-biased genes on the X chromosome and 2,466 on the autosomes, compared to 281 X-linked and 2,164 autosomal male-biased genes. Thus, 18.5% of all female-biased genes are located on the X chromosome, while for male-biased genes the corresponding number is only 11.5%. This difference is highly significant (P < 0.0001), demonstrating that the X chromosome is enriched for female-biased single transcript genes.
The same comparisons were performed for multi-transcript genes. There were 116 X-chromosomal and 616 autosomal genes that showed a significant difference in gene expression in either sex or line; these showed no statistical evidence for chromosomal bias (P = 0.9479). However, among genes that showed sex-biased transcript abundance, 78 X-linked and 304 autosomal genes were female-biased, compared to 38 X-linked and 312 autosomal genes that were male-biased. The proportions of female- and male-biased genes located on the X chromosome (20.4% and 10.9%, respectively) were significantly different (P = 0.0004), demonstrating that the X chromosome is enriched for female-biased multi-transcript genes.
We also tested whether sex-specific production of alternative transcripts (significant sex-by-probe interaction in the ANOVA model for multiple transcripts) was more likely to be observed for X-linked than for autosomal genes. There were 28 X-linked and 177 autosomal genes that showed significant sex-specific transcription; this proportion was not significantly different from that expected given the relative abundance of genes on the X chromosome and the autosomes (P = 0.3221). The male/female bias in alternative transcript representation was also independent of chromosomal location (P = 0.3479).
The benefits of microarray design based upon sequence similarity
Reliability of arrays (weighted kappa values ) based upon 20,265 probe spots
Overall (probes representing genes)
1 expected probe per cluster
2 to 5 expected probes per cluster
1 transcript per probe
2 to 5 transcripts per probe
Sex-specific gene expression
A very large fraction of the genome appears to be differentially expressed between males and females. In our experiments, 53% of all expressed genes (5,832 out of 10,933, including 291 unannotated genes) showed sex-biased expression. Other studies utilizing different microarray platforms produced very similar estimates [19, 37–42]. It is worth observing that all these studies, like ours, were performed in sexually mature, intact adults, and it is not surprising that gene expression profiles at this stage are dominated by the reproductive differences between males and females. It is clear, however, that most of the sexual dimorphism in gene expression is due to the germline. Comparisons of gonadectomized adults, or adults in which germ cells have been genetically ablated, produce much lower estimates of sexual dimorphism, on the order of 1.5% to 3% [1, 41]. Sexually dimorphic gene expression is much more prevalent in the germline than in the soma not only in Drosophila, but also in Caenorhabditis elegans [43–45] and in the mouse . This pattern is observed despite the differences in the mechanisms of sex determination in these taxa: in flies, the sex of each individual somatic cell is determined autonomously , whereas in mammals somatic sexual differentiation is controlled by a global hormonal mechanism .
We find that more genes show female-biased than male-biased expression (55% versus 45%). This result is in agreement with some of the previous reports , although other studies suggest that male-biased expression is more common than female-biased expression . The reasons for this contradiction are not clear, and could in principle include different lines, different microarray platforms, and/or different statistical approaches. However, many of the genes that showed significant differences in expression between males and females in our experiments were also found to be sexually dimorphic in other studies [19, 37–40]. Interestingly, we found that female-biased genes were much more likely to be located on the X chromosome than male-biased genes (18.5% versus 11.5% for single-transcript genes and 20.4% versus 10.9% for alternatively spliced genes; P < 0.0001). Similar 'feminization' of the X chromosome has previously been observed in Drosophila [40, 41] and C. elegans [44, 45].
We found that only two genes, modulo and CG33092, show significant sex differences that change depending on the line examined (that is, have genetic variation for sex dimorphism). In contrast, some earlier reports suggested that as much as 10% of the genome may show such sex-genotype interactions [37, 38]. This is despite the fact that the lines used in this study included the two parental lines used in one of these studies , as well as recombinant inbred lines derived from these two parents. The most likely reason for this is that significance thresholds used in our analysis were much more stringent than in previous reports. In fact, if we use the nominal significance threshold of 0.01, as in those reports, we find approximately the same proportions of genes showing sex-by-line interactions (not shown). We have chosen to report FDR-corrected thresholds since this approach incorporates an appropriate correction for multiple testing. It is also important to note that this study examines a limited number of lines, the two parents OregonR and 2b and six recombinant offspring from these two parents. The extent of alternative transcript production among lines will only be clear as more lines are sampled.
Evidence for functional consequences of alternative splicing
A large proportion of multi-exon genes in animal genomes are alternatively spliced, with estimates ranging from 30% to over 90% [20–24]. Alternative splicing is thought to make a significant contribution to phenotypic complexity by allowing a single locus to produce multiple, and possibly functionally distinct, proteins [49–52]. Supporting this view, many of the alternatively spliced genes in the human genome are spliced in a tissue-specific manner . In Drosophila, alternative splicing plays a prominent role in development, most notably by controlling sex determination [53–55]. In at least some Drosophila genes, alternative splicing is regulated in a sex-, tissue-, and/or stage-specific manner, so that different subsets of proteins encoded by the locus are produced in different developmental contexts [53, 56–61]. Alternatively spliced protein isoforms can, at least sometimes, have distinct functional specificities. For example, alternative isoforms of the lola transcription factor have different functional domains, and mutations affecting the different isoforms have distinct phenotypes . Similarly, one of the alternatively spliced transcripts of the Drosophila tyrosine hydroxylase (pale) is required for cuticle development, while a different transcript functions primarily in neurotransmission . One dramatic example of alternative splicing is the cell adhesion receptor Dscam, which may produce up to 38,016 splicing variants [63, 64]. Recent evidence indicates that specific isoforms function in distinct axon guidance pathways . However, evidence of the functional impact of alternative splicing remains largely anecdotal, and for the vast majority of genes functional comparisons between alternatively spliced variants are yet to be performed. At present, the extent to which alternative splicing contributes to functional protein diversity remains a matter of speculation. Exon-specific RNA interference  may finally allow this question to be addressed in a systematic manner.
We used the new microarray platform to estimate the extent of sex-specific production of alternative transcripts in the Drosophila genome. Approximately 22% of multi-transcript genes showed significant evidence that alternative transcripts were present in different ratios in males versus females. Some of these results might be experimental artifacts due to technical differences between probes, or mistakes in the current gene annotation. To address this concern, we used identified multiple probes that were predicted to hybridize to the same target transcripts as controls. Significant interactions between sex and probe will provide an estimate of the maximum proportion of significant tests that might be due to differences among probes, or problems with annotation. We found this proportion to be less than 10%, suggesting that at least 12% of all genes that produce alternative transcripts do so in a sex-specific manner. qPCR with primer pairs flanking alternative exon junctions confirmed sex-biased splicing for eight out of nine tested genes, indicating that exon-specific microarray probes provide a reliable means of detecting variation in the relative abundance of alternative transcripts. As in the case of sex-biased transcription, we suspect that much of the sex-specific splicing may be accounted for by reproductive tissues, and that most differences between males and females are likely to be quantitative rather than qualitative. Despite these qualifications, the prevalence of sexual differences in the production of alternative transcripts may have important functional consequences, and needs to be investigated in greater detail.
Sex-biased expression of splicing regulators
1.99 × 10-29
1.36 × 10-25
6.67 × 10-24
1.12 × 10-23
6.95 × 10-22
6.48 × 10-21
1.44 × 10-19
3.09 × 10-18
1.14 × 10-17
3.34 × 10-17
4.42 × 10-14
4.66 × 10-14
6.94 × 10-14
2.15 × 10-13
2.4 × 10-13
7.93 × 10-13
1.31 × 10-12
1.57 × 10-12
2.11 × 10-12
2.14 × 10-11
5.31 × 10-11
5.94 × 10-11
6.24 × 10-11
1.15 × 10-10
2.23 × 10-10
1.63 × 10-09
5.74 × 10-09
1.38 × 10-08
2.12 × 10-08
5.44 × 10-08
3.31 × 10-07
3.57 × 10-06
4.91 × 10-06
2.79 × 10-05
Materials and methods
Transcript clustering and probe design
For each singleton transcript, and each unique and common region of clustered transcripts, we designed at least one 60-mer oligonucleotide probe. For 1,929 common regions of sufficient length to support non-overlapping probes that fit our design criteria, we designed two probes per region. To select the probes, we examined all possible 60-mers for each of the target regions, and scored each candidate based on several criteria, including GC content, OligoArray 2.1 score , homopolymer length, dimer formation, and self-complementarity. Probes that satisfied all optimality criteria could be designed for all but 312 target regions. For those regions, multiple non-optimal probes were selected. All probes were examined to verify that they matched only the expected regions in the current version of Drosophila genome annotation, and subjected to a final BLAT verification. In particular, probes that were designed for singletons or unique regions were checked to make sure they did not match any other transcripts, whereas probes that were designed to represent common regions were confirmed to match only the expected set of transcripts.
Total number of probes
Total number of genes
Singletons 1 probe
Singletons >1 probe
We used the human genome to design 20 negative control probes according to the same criteria as the Drosophila probes. These probes were compared to the Drosophila genome sequence to verify that they had no sequence similarity to any D. melanogaster genes. Five of these negative controls were randomly chosen for printing, and each was placed on the microarray one hundred times. At the end of the design there were an additional 3 spots available upon which negative controls were placed, for a total of 503 negative control spots. These negative controls allow us to estimate the distribution of signal intensities for probes that fail to hybridize, and to make present/absent calls for each transcript.
The microarray printed according to our design by Agilent Technologies had a total of 22,575 spots, including 20,768 spots representing Drosophila transcripts, 503 negative control spots, and 1,304 Agilent controls (Table 6). These chips can be ordered from Agilent directly by quoting the AMADID number 012798.
Annotation and update procedure
Genome annotation changes as gene prediction methods improve and more experimental data become available. To allow the microarrays to be regularly updated to reflect these changes, we have written an automated annotation program that tracks the identity of each probe in the current version of FlyBase, and reports how many transcripts match this probe and whether this set is concordant with the expected design. We output all matches between probes and transcripts and then reduce this information to one row per probe, with a column that lists all matches for that probe. Detailed annotation is extracted for the first match, using CG numbers to identify which gene(s) are targeted by each probe. Other columns enumerate the number of transcripts predicted for that CG in the current annotation, the number of transcripts the particular probe matches, the number of probes for that CG in the current microarray design, and whether the probe aligns with the gene with which it was originally designed to align. In this last column, four different designations may be given: 'match' (probe aligns with the same CG as expected), 'mismatch' (a different CG than expected), 'extended' (same CG as expected, but the probe hits more transcripts of that CG than expected), and 'not found' (no matches to any transcripts in the current FlyBase). Since the initial design includes predicted but unconfirmed genes, we expect that some probes will not be found in the current database. Additionally, probes are categorized into one of the following groups: 'singletons' (one match per probe), 'gene families' (match to more than one CG number), 'alternative transcripts' (one CG number represented by multiple common and unique regions), and 'pseudo-clusters' (more than one probe representing a single transcript). If two or more probes in an alternative transcript or gene family hit the same target region in the current annotation file, these probes were considered part of a 'set'. Each such set can then be classified as 'global' (expected hybridization to all transcripts of a particular transcript identified by a CG designation), or 'local' (expected hybridization to a subset of alternative transcripts of a specific CG designation).
Drosophilalines and RNA sample preparation
Experiments were conducted on flies from two standard laboratory strains of D. melanogaster: OregonR  and 2b , and six randomly chosen recombinant inbred (RI) lines derived from these parental lines . Each of the 8 lines was grown in 4 separate replicates of small mass-matings containing, on average, 20 adults, with a sex ratio of 1:1. Bottles were maintained at 25°C with a 12:12 hour light:dark cycle, and the parents were removed after 3 days. We collected 20 virgin males and females within 24 hours from each replicate, transferred separately to fresh vials, and maintained for 3 days. After the maturation period, the virgin adult females and males were snap-frozen in liquid nitrogen for total RNA extraction.
RNA was extracted from each sample using Trizol reagent (Invitrogen Carlsbad, California, USA) according to the manufacturer's instructions, and purified using RNAeasy Kit (Qiagen, Valencia, CA, USA). RNA concentration was determined using NanoDrop Spectrophotometer (NanoDrop Technologies, Inc. Wilmington, DE USA), and the sample quality was examined using the Agilent 2100 Bioanalyzer (Agilent Technologies, Inc. Palo Alto, CA USA). We used 500 ng of RNA from each sample for the microarray experiment.
Microarray hybridization and signal detection
Fluorescent cRNA was synthesized using the Aglient low RNA input fluorescent linear amplification kit following the manufacturer's protocol. Briefly, first and second strand cDNA was synthesized from 500 ng total RNA using an oligo dT-promoter primer and reverse transcriptase. Next, cRNA was synthesized using a T7 RNA polymerase, which simultaneously incorporates cyanine 3- or cyanine 5-labeled CTP. Labeled RNA was cleaned using Qiagen RNeasy columns and cRNA yield was quantified on a NanoDrop ND-1000 spectrophotometer. We pooled 750 ng of each labeled sample and hybridized to the arrays following the manufacturer's protocol. Hybridizations were performed with males and females of the same line labeled in contrasting dyes and hybridized to the same chip. We analyzed four independent biological replicates for each line and sex combination. For two of these replicates, males were labeled with Cy3 and females with Cy5, whereas for the other two the dyes were reversed. No technical replicates were performed as reliability of the Agilent platform is, on average, above 90% (unpublished data by LMM, MLW, SVN, LH, AK). This design maximizes the ability to test for sex effects (NIH project 5R24GM065513), and ensures that effects of sex remain balanced in the event of chip failure.
Microarray experiments were carried out at the Interdisciplinary Center for Biotechnology Research Microarray Core, University of Florida. Hybridization occurred for 17 hours at 60°C in accordance with the manufacturer's instructions, and arrays were scanned using an Agilent Microarray scanner. There were seven technical failures, which were unrelated to the platform, leaving 25 successful hybridizations. Additionally, Agilent reported a manufacturing error that affected 2,310 spots on each chip, including 150 of the 503 negative controls. The failed chips and defective spots were removed from further consideration.
Images were analyzed using Imagene software version 6.0 at the Purdue University Genomics Database Facility. Spots were individually quantified, and the mean intensities and mean background signal corresponding to each spot were exported into .csv files. As with other chip analysis software, in Imagene, the units are a function of pixel intensity. Individual files were collated for analysis at the Purdue University Genomics Database Facility. Transcript abundance was estimated as the natural log of the spot mean minus the mean of the local background.
All spots on the array were compared between pairs of biological replicates to determine the reproducibility of RNA labeling and hybridization. Weighted kappa values ranged from 0.754 to 0.906, with a median of 0.85 (Table 4), indicating that our platform had high repeatability; in general, weighted kappa values above 0.75 are considered excellent . Following this overall assessment, we examined repeatability for subsets of probes to determine whether any of the known variables (including GC content, Tm, Oligoarray score, the number of probes per CG, the number of transcripts per probe, and whether the probe hybridized to multiple CGs) affected the reproducibility of hybridization. For most comparisons, these variables had little to no impact on the concordance among replicates. Additionally, the few probes that were designed outside of the usual stringent criteria did not perform worse than the optimally designed probes (median weighted kappa of 0.83). However, there were three large clusters of alternative transcripts (consisting of 11, 16, and 26 transcripts) that produced inconsistent results across replicates.
We then examined the distribution of signal intensities for the 353 negative control spots. These spots form the null distribution of intensity values for a given slide and dye combination in the absence of hybridization. Individual Drosophila probes were declared to have hybridized if the intensity of that spot was greater than the intensity of 95% of the negative controls for that slide and dye combination. Probes were considered to be detected for a particular treatment (that is, line/sex combination) if they hybridized in 50% or more of the replicates of that treatment. Probes that were not detected in at least one treatment were considered uninformative, and not considered further. The 20,265 available spots represented three groups of probes: Agilent controls (1,172 spots), negative hybridization controls (353 spots), and Drosophila probes (18,740 spots). There were 13,874 Drosophila probes (74%) found to hybridize in at least one treatment, including 187 of the 311 suboptimal probes (Table 6). Of the 2,156 probes designed for predicted genes, 963 showed detectable hybridization, confirming the existence of predicted transcripts. Of the 13,874 probes that were detected in at least one treatment, 5,128 represented alternative transcripts (2,479 genes), 436 represented gene families (162 genes), 45 represented pseudo-clusters (27 genes), and 8,265 represented singleton transcripts (8,265 genes). The data discussed in this publication have been deposited in NCBIs Gene Expression Omnibus and are accessible through GEO Series accession number GSE4976.
For genes that had more than one informative transcript, the following ANOVA model for multiple transcripts was fitted for each CG:
Y ijkl = μ + d i + t j + p k + tp jk + ε ijkl
where Y ijkl is the transcript abundance for dye i, treatment j, probe set k, and replicate l; μ is the overall mean of the transcript abundance for that CG; d is the dye effect; p is the effect of probe set; and ε is the error. A treatment (t) in this case is a combination of line and sex, and there were a total of 16 treatments since we examined 2 sexes for each of 8 lines. The ANOVA modeling approach compares means among groups, and determines whether the means are significantly different given the observed level of variation. To test whether a particular effect was statistically significant, we used the FDR approach , which is common in genomic research [81–85] (an introduction can be found in ). Briefly, the false discovery rate controls the proportion of false positives in the total list of tests rejected. Thus, if 100 tests are rejected, and the FDR is set to 0.05, the expected number of false positives is 5. First, we tested the main effect of treatment (t j ). That is, we tested whether the means were different among any of the 16 line/sex combinations (treatments). If this test was significant at FDR = 0.05, we declared this gene significant and investigated further whether the differences were due to sex, line, or interaction between sex and line effects at a very strict FDR of 0.05/3. To determine whether the relative amounts of alternative transcripts differed among sexes or lines, we tested the interaction between probe set and treatment (tp jk ) and, if it was significant at FDR = 0.05, we further examined whether this was due to interaction between probe and sex or probe and line effects. For cases where the main effect of probe set (p k ) was significant, we compared the effect of 'global' probes to each 'local' probe. The multiple transcript model was also fitted for gene families.
Significant probe-by-sex or probe-by-line interactions might arise not only as a consequence of genetic variation in alternative transcript production, but also as an artifact of intrinsic differences between probes. In order to estimate the rate of such artifacts, we used the model above to examine sets of probes that were expected to hybridize to the same transcript or group of transcripts (that is, the same unique region or the same common region). For such sets of probes, their relative intensities should, in principle, be identical in all treatments, and thus no significant probe by treatment interactions should be observed. By measuring the actual proportion of the control probe sets for which probe by treatment interaction is significant, we can estimate the rate of putative false positives. However, it should be noted that the expected hybridization targets of the probe sets are defined based on the current annotation, and it is possible that some of the probes are in fact hybridizing to different transcripts or sets of transcripts. Thus, this approach will probably over-estimate the number of false positives.
For genes that had a single informative transcript, the following ANOVA model for single transcripts was fitted for each transcript individually:
Y ijl = μ + d i + t j + ε ijl
Where Yijl is the transcript abundance for dye i, treatment j, and replicate l; μ is the overall mean of the transcript abundance for that transcript; d is the dye effect; and ε is the error. As above, a treatment (t) in this case is a combination of line and sex, and there were a total of 16 treatments since we examined two sexes for each of 8 lines. [87–92]. Significance testing was performed as described above. All analyses were performed using SAS software version 9.1 (SAS Institute, Cary, NC, USA).
Quantitative PCR analysis for data validation
Total RNA was isolated from whole virgin adults of the Oregon-R line as described above. For each sex, we used three biological replicates of four individuals each. To correct for differences in transcript abundance between sexes, samples were equalized by evaporation and resuspension in DEPC-treated water (DEPC: Diethyl pyrocarbonate). DNase I digestion (NEB, Ipswich, MA, USA) was carried out for 30 minutes at 37°C. Reverse transcription was performed on 5 μg of total RNA using oligo(dT)16, as described by the manufacturer (Applied Biosystems, Foster City, CA, USA). qPCR was performed on 100 ng of cDNA product in a total volume of 25 μl using TaqMan PCR Mix (Applied Biosystems). Primers for qPCR were designed to amplify either constitutive or alternative exon junctions of specific transcripts listed in Additional file 6. PCR amplification was detected using SYBR® Green I dye chemistry and ABI Prism 7900 Real Time PCR system (Applied Biosystems). CTs were determined using the AB7900 system SDS software and defined as the fluorescence intensity significantly above background during the exponential phase of amplification for all reactions. For each gene, CT values were analyzed using the ANOVA model:
Y ijk = μ + s i + p j + sp ij + ε ijk
where Y ij is cycle count for the i th sex and j th transcript for replicate k; μ is the overall mean for that gene and ε is the random error. Specifically, we tested whether the sex by transcript interaction effect was significant at a nominal level of 0.05.
The following additional data are available with the online version of this paper. Additional data file 1 includes the microarray results for several sets of genes for which we had a priori expectations of sex-biased expression. Additional data file 2 includes the processed microarray data used for analysis, as well as annotation from FlyBase from our AAP program. Actual_set_id is the unique identifier for each probe that hybridizes to the same set of transcripts, and actuals_cluster_id is a unique identifier that groups probes based upon CG number. Probeuid is the unique identifier for that probe sequence. Additional data file 3 provides results of the analysis, as well as annotations from FlyBase. The P values obtained from the ANOVA are given with the notation p<effect>. The CG number is given in actuals and the model used for analysis (Single transcript/multiple transcript) is given in the final column. Additional data file 4 gives the results of analysis based upon the probe level, as well as annotations from FlyBase. Additional data file 5 provides the probe-by-probe analysis of alternatively spliced genes analyzed using ANOVA model for multiple transcripts. The columns are, in order: probe ID; gene name; whether hybridization signal detected by that probe is greater in males or females; log-transformed female/male expression ratio for each probe; probe set ID; class of probe (global or local); P value for the sex by probe set interaction; and the list of transcripts targeted by each probe. See text for further details. Additional data file 6 includes the qPCR validation of sex-specific splicing. We give the probe sequences used, all qPCR results as well as the original array results to facilitate comparison. The P values of the likelihood ratio test (LRT) for a significant probe-sex interaction are also given. Note that for genes where only one transcript was tested, the test of the interaction between transcript and sex is not applicable (NA).
This work was supported by an NIH-GLUE grant 5R24GM065513 to SVN, LMM, MLW, LH, AK, and by the Purdue University Genomics Database Facility.
- Arbeitman MN, Fleming AA, Siegal ML, Null BH, Baker BS: A genomic analysis of Drosophila somatic sexual differentiation and its regulation. Development. 2004, 131: 2007-2021. 10.1242/dev.01077.PubMedView ArticleGoogle Scholar
- Klebes A, Sustar A, Kechris K, Li H, Schubiger G, Kornber TB: Regulation of cellular plasticity in Drosophila imaginal disc cells by the Polycomb group, trithorax group and lama genes. Development. 2005, 132: 3753-3765. 10.1242/dev.01927.PubMedView ArticleGoogle Scholar
- Reeves N, Posakony JW: Genetic programs activated by proneural proteins in the developing Drosophila PNS. Dev Cell. 2005, 8: 413-425. 10.1016/j.devcel.2005.01.020.PubMedView ArticleGoogle Scholar
- Stathopoulos A, Van Drenth M, Erives A, Markstein M, Levine M: Whole-genome analysis of dorsal-ventral patterning in the Drosophila embryo. Cell. 2002, 111: 687-701. 10.1016/S0092-8674(02)01087-5.PubMedView ArticleGoogle Scholar
- Stathopoulos A, Levine M: Whole-genome analysis of Drosophila gastrulation. Curr Opin Genet Dev. 2004, 14: 477-484. 10.1016/j.gde.2004.07.004.PubMedView ArticleGoogle Scholar
- Cirelli C, Lavaute TM, Tononi G: Sleep and wakefulness modulate gene expression in Drosophila. J Neurochem. 2005, 94: 1411-9. 10.1111/j.1471-4159.2005.03291.x.PubMedView ArticleGoogle Scholar
- Duffield GE: DNA microarray analyses of circadian timing: the genomic basis of biological time. J Neuroendocrinol. 2003, 15: 991-1002. 10.1046/j.1365-2826.2003.01082.x.PubMedView ArticleGoogle Scholar
- Whitfield CW, Cziko AM, Robinson GE: Gene expression profiles in the brain predict behavior in individual honey bees. Science. 2003, 302: 296-299. 10.1126/science.1086807.PubMedView ArticleGoogle Scholar
- Meiklejohn CD, Parsch J, Ranz JM, Hartl DL: Rapid evolution of male-biased gene expression in Drosophila. Proc Natl Acad Sci USA. 2003, 100: 9894-9899. 10.1073/pnas.1630690100.PubMedPubMed CentralView ArticleGoogle Scholar
- Ranz JM, Namgyal K, Gibson G, Hartl DL: Anomalies in the expression profile of interspecific hybrids of Drosophila melanogaster and Drosophila simulans. Genome Res. 2004, 14: 373-379. 10.1101/gr.2019804.PubMedPubMed CentralView ArticleGoogle Scholar
- Michalak P, Noor MAF: Association of misexpression with sterility in hybrids of Drosophila simulans and D. mauritiana. J Mol Evol. 2004, 59: 277-282. 10.1007/s00239-004-2622-y.PubMedView ArticleGoogle Scholar
- Nuzhdin SV, Wayne ML, Harmon KL, McIntyre LM: Common pattern of evolution of gene expression level and protein sequence in Drosophila. Mol Biol Evol. 2004, 21: 1308-1317. 10.1093/molbev/msh128.PubMedView ArticleGoogle Scholar
- Wayne ML, Pan Y-J, Nuzhdin SV, McIntyre LM: Additivity and trans-acting effects on expression in male Drosophila simulans. Genetics. 2004, 168: 1413-1420. 10.1534/genetics.104.030973.PubMedPubMed CentralView ArticleGoogle Scholar
- Stern DL: Perspective: Evolutionary developmental biology and the problem of variation. Evolution. 2000, 54: 1079-1091. 10.1554/0014-3820(2000)054[1079:PEDBAT]2.0.CO;2.PubMedView ArticleGoogle Scholar
- Anholt RRH, Mackay TFC: The genetic architecture of odor-guided behavior in Drosophila melanogaster. Behav Genet. 2001, 31: 17-27. 10.1023/A:1010201723966.PubMedView ArticleGoogle Scholar
- Rockman MV, Wray GA: Abundant raw material for cis-regulatory evolution in humans. Mol Biol Evol. 2002, 19: 1991-2004.PubMedView ArticleGoogle Scholar
- Coffman CJ, Wayne ML, Nuzhdin SV, Higgins LA, McIntyre LM: Identification of co-regulated transcripts affecting male body size in Drosophila. Genome Biol. 2005, 6: R53-10.1186/gb-2005-6-6-r53.PubMedPubMed CentralView ArticleGoogle Scholar
- Mackay TFC, Heinsohn SL, Lyman RF, Moehring AJ, Morgan TJ, Rollmann SM: Genetics and genomics of Drosophila mating behavior. Proc Natl Acad Sci USA. 2005, 102: 6622-6629. 10.1073/pnas.0501986102.PubMedPubMed CentralView ArticleGoogle Scholar
- Harbison ST, Chang S, Kamdar KP, Mackay TFC: Quantitative genomics of starvation stress resistance in Drosophila. Genome Biol. 2005, 6: R36-10.1186/gb-2005-6-4-r36.PubMedPubMed CentralView ArticleGoogle Scholar
- Brett D, Hanke J, Lehmann G, Haase S, Delbruck S, Krueger S, Reich J, Bork P: EST comparison indicates 38% of human mRNAs contain possible alternative splice forms. FEBS Letters. 2000, 474: 83-86. 10.1016/S0014-5793(00)01581-7.PubMedView ArticleGoogle Scholar
- Modrek B, Resch A, Grasso C, Lee C: Genome-wide detection of alternative splicing in expressed sequences of human genes. Nucleic Acids Res. 2001, 29: 2850-2859. 10.1093/nar/29.13.2850.PubMedPubMed CentralView ArticleGoogle Scholar
- Gupta S, Zink D, Korn B, Vingron M, Haas SA: Genome wide identification and classification of alternative splicing based on EST data. Bioinformatics. 2004, 20: 2579-2585. 10.1093/bioinformatics/bth288.PubMedView ArticleGoogle Scholar
- Johnson JM, Castle J, Garrett-Engele P, Kan ZY, Loerch PM, Armour CD, Santos R, Schadt EE, Stoughton R, Shoemaker DD: Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science. 2003, 302: 2141-2144. 10.1126/science.1090100.PubMedView ArticleGoogle Scholar
- Mironov AA, Fickett JW, Gelfand MS: Frequent alternative splicing of human genes. Genome Res. 1999, 9: 1288-1293. 10.1101/gr.9.12.1288.PubMedPubMed CentralView ArticleGoogle Scholar
- Xu A, Park SK, D'Mello S, Kim E, Wang Q, Pikielny CW: Novel genes expressed in subsets of chemosensory sensilla on the front legs of male Drosophila melanogaster. Cell Tissue Res. 2002, 307: 381-392. 10.1007/s00441-002-0524-0.PubMedView ArticleGoogle Scholar
- Baker BS, Nagoshi RN, Burtis KC: Molecular genetic-aspects of sex determination in Drosophila. Bioessays. 1987, 6: 66-70. 10.1002/bies.950060206.PubMedView ArticleGoogle Scholar
- Belote JM, Handler AM, Wolfner MF, Livak KJ, Baker BS: Sex-specific regulation of yolk protein gene-expression in Drosophila. Cell. 1985, 40: 339-348. 10.1016/0092-8674(85)90148-5.PubMedView ArticleGoogle Scholar
- Burtis KC, Baker BS: Drosophila Doublesex gene controls somatic sexual-differentiation by producing alternatively spliced messenger-RNAs encoding related sex-specific polypeptides. Cell. 1989, 56: 997-1010. 10.1016/0092-8674(89)90633-8.PubMedView ArticleGoogle Scholar
- Chapman KB, Wolfner MF: Determination of male-specific gene-expression in Drosophila accessory-glands. Dev Biol. 1988, 126: 195-202. 10.1016/0012-1606(88)90253-9.PubMedView ArticleGoogle Scholar
- Wolfner MF: Tokens of love: Functions and regulation of Drosophila male accessory gland products. Insect Biochem Mol Biol. 1997, 27: 179-192. 10.1016/S0965-1748(96)00084-7.PubMedView ArticleGoogle Scholar
- Wolfner MF, Harada HA, Bertram MJ, Stelick TJ, Kraus KW, Kalb JM, Lung YO, Neubaum DM, Park M, Tram U: New genes for male accessory gland proteins in Drosophila melanogaster. Insect Biochem Mol Biol. 1997, 27: 825-834. 10.1016/S0965-1748(97)00056-8.PubMedView ArticleGoogle Scholar
- Carvalho AB, Lazzaro BP, Clark AG: Y chromosomal fertility factors kl-2 and kl-3 of Drosophila melanogaster encode dynein heavy chain polypeptides. Proc Natl Acad Sci USA. 2000, 97: 13239-13244. 10.1073/pnas.230438397.PubMedPubMed CentralView ArticleGoogle Scholar
- Dibenedetto AJ, Lakich DM, Kruger WD, Belote JM, Baker BS, Wolfner MF: Sequences expressed sex-specifically in Drosophila melanogaster adults. Dev Biol. 1987, 119: 242-251. 10.1016/0012-1606(87)90225-9.PubMedView ArticleGoogle Scholar
- Schafer U: Genes for male-specific transcripts in Drosophila melanogaster. Mol General Genet. 1986, 202: 219-225. 10.1007/BF00331640.View ArticleGoogle Scholar
- Bell LR, Maine EM, Schedl P, Cline TW: Sex-lethal, a Drosophila sex determination switch gene, exhibits sex-specific RNA splicing and sequence similarity to RNA-binding proteins. Cell. 1988, 55: 1037-1046. 10.1016/0092-8674(88)90248-6.PubMedView ArticleGoogle Scholar
- FlyBase. [http://www.flybase.org]
- Jin W, Riley RM, Wolfinger RD, White KP, Passador-Gurgel G, Gibson G: The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat Genet. 2001, 29: 389-395. 10.1038/ng766.PubMedView ArticleGoogle Scholar
- Gibson G, Riley-Berger R, Harshman LG, Kopp A, Nuzhdin SV, Wayne ML: Extensive sex-specific non-additivity in gene expression in Drosophila melanogaster. Genetics. 2004, 167: 1791-1799. 10.1534/genetics.104.026583.PubMedPubMed CentralView ArticleGoogle Scholar
- Ranz JM, Castillo-Davis CI, Meiklejohn CD, Hartl DL: Sex-dependent gene expression and evolution of the Drosophila transcriptome. Science. 2003, 300: 1742-1745. 10.1126/science.1085881.PubMedView ArticleGoogle Scholar
- Parisi M, Nuttall R, Naiman D, Bouffard G, Malley J, Andrews J, Eastman S, Oliver B: Paucity of genes on the Drosophila X chromosome showing male-biased expression. Science. 2003, 299: 697-700. 10.1126/science.1079190.PubMedPubMed CentralView ArticleGoogle Scholar
- Parisi M, Nuttall R, Edwards P, Minor J, Naiman D, Lu JN, Doctolero M, Vainer M, Chan C, Malley J, et al: A survey of ovary-, testis-, and soma-biased gene expression in Drosophila melanogaster adults. Genome Biol. 2004, 5: R40-10.1186/gb-2004-5-6-r40.PubMedPubMed CentralView ArticleGoogle Scholar
- Arbeitman MN, Furlon EE, Imam F, Johnson E, Null BH, Baker BS, Krasnow MA, Scott MP, Davis RW, White KP: Gene expression during the life cycle of Drosophila melanogaster. Science. 2002, 297: 2270-2275. 10.1126/science.1072152.PubMedView ArticleGoogle Scholar
- Jiang M, Ryu J, Kiraly M, Duke K, Reinke V, Kim SK: Genome-wide analysis of developmental and sex-regulated gene expression profiles in Caenorhabditis elegans. Proc Natl Acad Sci USA. 2001, 98: 218-223. 10.1073/pnas.011520898.PubMedPubMed CentralView ArticleGoogle Scholar
- Reinke V, Smith HE, Nance J, Wang J, Van Doren C, Begley R, Jones SJM, Davis EB, Scherer S, Ward S, et al: A global profile of germline gene expression in C. elegans. Mol Cell. 2000, 6: 605-616. 10.1016/S1097-2765(00)00059-9.PubMedView ArticleGoogle Scholar
- Reinke V, Gil IS, Ward S, Kazmer K: Genome-wide germline-enriched and sex-biased expression profiles in Caenorhabditis elegans. Development. 2004, 131: 311-323. 10.1242/dev.00914.PubMedView ArticleGoogle Scholar
- Rinn JL, Rozowsky JS, Laurenzi IJ, Petersen PH, Zou KY, Zhong WM, Gerstein M, Snyder M: Major molecular differences between mammalian sexes are involved in drug metabolism and renal function. Dev Cell. 2004, 6: 791-800. 10.1016/j.devcel.2004.05.005.PubMedView ArticleGoogle Scholar
- Baker BS, Ridge KA: Sex and the single cell.1. On the action of major loci affecting sex determination in Drosophila melanogaster. Genetics. 1980, 94: 383-423.PubMedPubMed CentralGoogle Scholar
- Swain A, Lovell-Badge R: Mammalian sex determination: a molecular drama. Genes Dev. 1999, 13: 755-767.PubMedView ArticleGoogle Scholar
- Graveley BR: Alternative splicing: increasing diversity in the proteomic world. Trends Genet. 2001, 17: 100-107. 10.1016/S0168-9525(00)02176-4.PubMedView ArticleGoogle Scholar
- Brett D, Pospisil H, Valcarel J, Reich J, Bork P: Alternative splicing and genome complexity. Nat Genet. 2002, 30: 29-30. 10.1038/ng803.PubMedView ArticleGoogle Scholar
- Roberts GC, Smith CWJ: Alternative splicing: combinatorial output from the genome. Curr Opin Chem Biol. 2002, 6: 375-383. 10.1016/S1367-5931(02)00320-4.PubMedView ArticleGoogle Scholar
- Zavolan M, Kondo S, Schonbach C, Adachi J, Hume DA, Hayashizaki Y, Gaasterland T: Impact of alternative initiation, splicing, and termination on the diversity of the mRNA transcripts encoded by the mouse transcriptome. Genome Res. 2003, 13: 1290-1300. 10.1101/gr.1017303.PubMedPubMed CentralView ArticleGoogle Scholar
- Nagoshi RN, McKeown M, Burtis KC, Belote JM, Baker BS: The control of alternative splicing at genes regulating sexual-differentiation in Drosophila melanogaster. Cell. 1988, 53: 229-236. 10.1016/0092-8674(88)90384-4.PubMedView ArticleGoogle Scholar
- Bell LR, Horabin JI, Schedl P, Cline TW: Positive autoregulation of sex-lethal by alternative splicing maintains the female determined state in Drosophila. Cell. 1991, 65: 229-239. 10.1016/0092-8674(91)90157-T.PubMedView ArticleGoogle Scholar
- McKeown M: Sex differentiation: The role of alternative splicing. Curr Opin Genet Dev. 1992, 2: 299-303. 10.1016/S0959-437X(05)80288-6.PubMedView ArticleGoogle Scholar
- Anand A, Villella A, Ryner LC, Carlo T, Goodwin SF, Song HJ, Gailey DA, Morales A, Hall JC, Baker BS, et al: Molecular genetic dissection of the sex-specific and vital functions of the Drosophila melanogaster sex determination gene fruitless. Genetics. 2001, 158: 1569-1595.PubMedPubMed CentralGoogle Scholar
- Goeke S, Greene EA, Grant PK, Gates MA, Crowner D, Aigaki T, Giniger E: Alternative splicing of lola generates 19 transcription factors controlling axon guidance in Drosophila. Nat Neurosci. 2003, 6: 917-924. 10.1038/nn1105.PubMedView ArticleGoogle Scholar
- Hess NK, Bernstein SI: Developmentally regulated alternative splicing of Drosophila myosin heavy-chain transcripts - in vivo analysis of an unusual 3'-splice site. Dev Biol. 1991, 146: 339-344. 10.1016/0012-1606(91)90235-U.PubMedView ArticleGoogle Scholar
- Horiuchi T, Giniger E, Aigaki T: Alternative trans-splicing of constant and variable exons of a Drosophila axon guidance gene, lola. Genes Dev. 2003, 17: 2496-2501. 10.1101/gad.1137303.PubMedPubMed CentralView ArticleGoogle Scholar
- Kramerova IA, Kramerov AA, Fessler JH: Alternative splicing of papilin and the diversity of Drosophila extracellular matrix during embryonic morphogenesis. Dev Dyn. 2003, 226: 634-642. 10.1002/dvdy.10265.PubMedView ArticleGoogle Scholar
- Tsitilou SG, Grammenoudi S: Evidence for alternative splicing and developmental regulation of the Drosophila melanogaster Mgat2 (N-acetylglucosaminyltransferase II) gene. Biochem Biophys Res Comm. 2003, 312: 1372-1376. 10.1016/j.bbrc.2003.11.059.PubMedView ArticleGoogle Scholar
- Friggi-Grelin F, Iche M, Birman S: Tissue-specific developmental requirements of Drosophila tyrosine hydroxylase isoforms. Genesis. 2003, 35: 260-269. 10.1002/gene.1082.PubMedView ArticleGoogle Scholar
- Schmucker D, Clemens JC, Shu H, Worby CA, Xiao J, Muda M, Dixon JE, Zipursky SL: Drosophila Dscam is an axon guidance receptor exhibiting extraordinary molecular diversity. Cell. 2000, 101: 671-684. 10.1016/S0092-8674(00)80878-8.PubMedView ArticleGoogle Scholar
- Zhan XL, Clemens JC, Neves G, Hattori D, Flanagan JJ, Hummel T, Vasconcelos ML, Chess A, Zipursky SL: Analysis of Dscam diversity in regulating axon guidance in Drosophila mushroom bodies. Neuron. 2004, 43: 673-686. 10.1016/j.neuron.2004.07.020.PubMedView ArticleGoogle Scholar
- Chen BE, Kondo M, Garnier A, Watson FL, Puettmann-Holgado R, Lamar DR, Schmucker D: The molecular diversity of Dscam is functionally required for neuronal wiring specificity in Drosophila. Cell. 2006, 125: 607-620. 10.1016/j.cell.2006.03.034.PubMedView ArticleGoogle Scholar
- Celotto AM, Lee JW, Graveley BR: Exon-specific RNA interference: A tool to determine the functional relevance of proteins encoded by alternatively spliced mRNAs. Methods Mol Biol. 2005, 309: 273-282.PubMedPubMed CentralGoogle Scholar
- Park JW, Parisky K, Celotto AM, Reenan RA, Graveley BR: Identification of alternative splicing regulators by RNA interference in Drosphila. Proc Natl Acad Sci USA. 2004, 101: 15974-15979. 10.1073/pnas.0407004101.PubMedPubMed CentralView ArticleGoogle Scholar
- Deshpande G, Samuels ME, Schedl PD: Sex-lethal interacts with splicing factors in vitro and in vivo. Mol Cell Biol. 1996, 16: 5036-5047.PubMedPubMed CentralView ArticleGoogle Scholar
- Lynch KW, Maniatis T: Assembly of specific SR protein complexes on distinct regulatory elements of the Drosophila doublesex splicing enhancer. Genes Dev. 1996, 10: 2089-2101.PubMedView ArticleGoogle Scholar
- Salz HK, Flickinger TW: Both loss-of-function and gain-of-function mutations in snf define a role for snRNP proteins in regulating Sex-lethal pre-mRNA splicing in Drosophila development. Genetics. 1996, 144: 95-108.PubMedPubMed CentralGoogle Scholar
- Tian M, Maniatis T: A splicing enhancer complex controls alternative splicing of doublesex pre-mRNA. Cell. 1993, 74: 105-114. 10.1016/0092-8674(93)90298-5.PubMedView ArticleGoogle Scholar
- Hild M, Beckmann B, Haas SA, Koch B, Solovyev V, Busold C, Fellenberg K, Boutros M, Vingron M, Sauer F, et al: An integrated gene annotation and transcriptional profiling approach towards the full gene content of the Drosophila genome. Genome Biol. 2003, 5: R3-10.1186/gb-2003-5-1-r3.PubMedPubMed CentralView ArticleGoogle Scholar
- Kent WJ: BLAT - The BLAST-like alignment tool. Genome Res. 2002, 12: 656-664. 10.1101/gr.229202. Article published online before March 2002.PubMedPubMed CentralView ArticleGoogle Scholar
- Thompson JD, Higgins DG, Gibson TJ: Clustal-W - improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994, 22: 4673-4680.PubMedPubMed CentralView ArticleGoogle Scholar
- Rouillard JM, Zuker M, Gulari E: OligoArray 2.0: design of oligonucleotide probes for DNA microarrays using a thermodynamic approach. Nucleic Acids Res. 2003, 31: 3057-3062. 10.1093/nar/gkg426.PubMedPubMed CentralView ArticleGoogle Scholar
- Lindsley DL, Zimm G: The Genome of Drosophila melanogaster. 1992, San Diego: Academic Press, IncGoogle Scholar
- Pasyukova EG, Nuzhdin SV: Doc and Copia instability in an isogenic Drosophila melanogaster stock. Mol General Genet. 1993, 240: 302-306. 10.1007/BF00277071.View ArticleGoogle Scholar
- Nuzhdin SV, Pasyukova EG, Dilda CL, Zeng ZB, Mackay TFC: Sex-specific quantitative trait loci affecting longevity in Drosophila melanogaster. Proc Natl Acad Sci USA. 1997, 94: 9734-9739. 10.1073/pnas.94.18.9734.PubMedPubMed CentralView ArticleGoogle Scholar
- Fleiss JL: Statistical Methods for Rates and Proportions. 1981, New York: John Wiley and SonsGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate - a practical and powerful approach to multiple testing. J Roy Stat Soc B Methodol. 1995, 57: 289-300.Google Scholar
- Weller JI, Song JZ, Heyen DW, Lewin HA, Ron M: A new approach to the problem of multiple comparisons in the genetic dissection of complex traits. Genetics. 1998, 150: 1699-1706.PubMedPubMed CentralGoogle Scholar
- Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001, 98: 10515-10515. 10.1073/pnas.091062498.View ArticleGoogle Scholar
- Storey JD, Tibshirani R: Statistical significance for genomewide studies. Proc Natl Acad Sci USA. 2003, 100: 9440-9445. 10.1073/pnas.1530509100.PubMedPubMed CentralView ArticleGoogle Scholar
- Sabatti C, Service S, Freimer N: False discovery rate in linkage and association genome screens for complex disorders. Genetics. 2003, 164: 829-833.PubMedPubMed CentralGoogle Scholar
- Peng JH, Ronin Y, Fahima T, Roder MS, Li YC, Nevo E, Korol A: Domestication quantitative trait loci in Triticum dicoccoides, the progenitor of wheat. Proc Natl Acad Sci USA. 2003, 100: 2489-2494. 10.1073/pnas.252763199.PubMedPubMed CentralView ArticleGoogle Scholar
- Verhoeven KJF, Simonsen KL, McIntyre L: Implementing false discovery rate control: increasing your power. Oikos. 2005, 109: 208-208. 10.1111/j.0030-1299.2005.13426.x.View ArticleGoogle Scholar
- Wayne ML, McIntyre LM: Combining mapping and arraying: An approach to candidate gene identification. Proc Natl Acad Sci USA. 2002, 99: 14903-14906. 10.1073/pnas.222549199.PubMedPubMed CentralView ArticleGoogle Scholar
- Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS: Assessing gene significance from cDNA microarray expression data via mixed models. J Comp Biol. 2001, 8: 625-637. 10.1089/106652701753307520.View ArticleGoogle Scholar
- Oleksiak MF, Churchill GA, Crawford DL: Variation in gene expression within and among natural populations. Nat Genet. 2002, 32: 261-266. 10.1038/ng983.PubMedView ArticleGoogle Scholar
- Singh AK, McIntyre LM, Sherman LA: Microarray analysis of the genome-wide response to iron deficiency and iron reconstitution in the cyanobacterium Synechocystis sp PCC 6803. Plant Physiol. 2003, 132: 1825-1839. 10.1104/pp.103.024018.PubMedPubMed CentralView ArticleGoogle Scholar
- Kerr MK, Churchill GA: Experimental design for gene expression microarrays. Biostatistics. 2001, 2: 183-201. 10.1093/biostatistics/2.2.183.PubMedView ArticleGoogle Scholar
- Kerr MK, Churchill GA: Statistical design and the analysis of gene expression microarray data. Genetical Res. 2001, 77: 123-128. 10.1017/S0016672301005055.Google Scholar
- Design of a Whole Genome Drosophila Chip. [http://www.genomics.purdue.edu/services/droschip/]
- Agilent Annotation Program. [http://www.genomics.purdue.edu/services/software/aap.shtml]
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