BackgroundStructural rearrangements of the genome resulting in genic imbalance due to copy number change are often deleterious at the organismal level, but are common in immortalized cell lines and tumors, where they may be an advantage to cells. In order to explore the biological consequences of copy number changes in the Drosophila genome, we resequenced the genomes of 19 tissue-culture cell lines and generated RNA-Seq profiles.ResultsOur work revealed dramatic duplications and deletions in all cell lines. We found three lines of evidence indicating that copy number changes were due to selection during tissue culture. First, we found that copy numbers correlated to maintain stoichiometric balance in protein complexes and biochemical pathways, consistent with the gene balance hypothesis. Second, while most copy number changes were cell line-specific, we identified some copy number changes shared by many of the independent cell lines. These included dramatic recurrence of increased copy number of the PDGF/VEGF receptor, which is also over-expressed in many cancer cells, and of bantam, an anti-apoptosis miRNA. Third, even when copy number changes seemed distinct between lines, there was strong evidence that they supported a common phenotypic outcome. For example, we found that proto-oncogenes were over-represented in one cell line (S2-DRSC), whereas tumor suppressor genes were under-represented in another (Kc167).ConclusionOur study illustrates how genome structure changes may contribute to selection of cell lines in vitro. This has implications for other cell-level natural selection progressions, including tumorigenesis.Electronic supplementary materialThe online version of this article (doi:10.1186/gb-2014-15-8-r70) contains supplementary material, which is available to authorized users.

BackgroundA generally accepted approach to the analysis of RNA-Seq read count data does not yet exist. We sequenced the mRNA of 726 individuals from the Drosophila Genetic Reference Panel in order to quantify differences in gene expression among single flies. One of our experimental goals was to identify the optimal analysis approach for the detection of differential gene expression among the factors we varied in the experiment: genotype, environment, sex, and their interactions. Here we evaluate three different filtering strategies, eight normalization methods, and two statistical approaches using our data set. We assessed differential gene expression among factors and performed a statistical power analysis using the eight biological replicates per genotype, environment, and sex in our data set.ResultsWe found that the most critical considerations for the analysis of RNA-Seq read count data were the normalization method, underlying data distribution assumption, and numbers of biological replicates, an observation consistent with previous RNA-Seq and microarray analysis comparisons. Some common normalization methods, such as Total Count, Quantile, and RPKM normalization, did not align the data across samples. Furthermore, analyses using the Median, Quantile, and Trimmed Mean of M-values normalization methods were sensitive to the removal of low-expressed genes from the data set. Although it is robust in many types of analysis, the normal data distribution assumption produced results vastly different than the negative binomial distribution. In addition, at least three biological replicates per condition were required in order to have sufficient statistical power to detect expression differences among the three-way interaction of genotype, environment, and sex.ConclusionsThe best analysis approach to our data was to normalize the read counts using the DESeq method and apply a generalized linear model assuming a negative binomial distribution using either edgeR or DESeq software. Genes having very low read counts were removed after normalizing the data and fitting it to the negative binomial distribution. We describe the results of this evaluation and include recommended analysis strategies for RNA-Seq read count data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2353-z) contains supplementary material, which is available to authorized users.

Nuclear pore complexes (NPCs) are important for cellular functions beyond nucleocytoplasmic trafficking, including genome organization and gene expression. This multi-faceted nature and the slow turnover of NPC components complicates investigations of how individual nucleoporins act in these diverse processes. To address this question, we apply an Auxin-Induced Degron (AID) system to distinguish roles of basket nucleoporins NUP153, NUP50 and TPR. Acute depletion of TPR causes rapid and pronounced changes in transcriptomic profiles. These changes are dissimilar to shifts observed after loss of NUP153 or NUP50, but closely related to changes caused by depletion of mRNA export receptor NXF1 or the GANP subunit of the TRanscription-EXport-2 (TREX-2) mRNA export complex. Moreover, TPR depletion disrupts association of TREX-2 subunits (GANP, PCID2, ENY2) to NPCs and results in abnormal RNA transcription and export. Our findings demonstrate a unique and pivotal role of TPR in gene expression through TREX-2- and/or NXF1-dependent mRNA turnover.