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Lightning talk presentation for Collaboration Workshop 2023 (CW23) https://software.co.uk/cw23. There is a growing gap between the levels of technical expertise in academia and industry. I outline what I believe would be a better use of available resources to address this situation.

Additional information containing online methods is provided as a PDF file. Figure S1. The effect of training size on predictions. A test set of 100 samples was used for all training sample sizes. Figure S2. Out-of-bag (OOB) filtering. Scatter plot of cross-sample correlation coefficients from on OOB gene expression estimates and estimates obtained on test data for 1,000 randomly selected genes. Figure S3. Comparison of differential expression results. Comparison of the performance of MaLTE and median-polish on the problem of detecting differential gene expression between five heart-muscle and five skeletal-muscle samples in the GTEx dataset. Each method results in a list of genes, ranked by the q-value from the comparison of the gene expression level in the two groups of samples. We used the (a) cumulative Jaccard index and (b) concordance correlation to compare the similarity as a function of rank and the overall similarity, respectively, between lists of genes ranked by MaLTE/median-polish and by RNA-seq. The set of genes assessed were defined by RNA-Seq: genes with RPKM of above one in both tissues. Bootstrap re-sampling (100 pseudo-replicates) was used to assess the effect of sampling error for both cumulative Jaccard index and concordance correlations. Results of bootstrapping are shown as faint lines around the observed Jaccard index and yield the density plots shown in (b). Log of fold change estimates for (c) 120 differentially expressed genes common to MaLTE and median-polish and (d) MaLTE and PLIER for six common genes. Figure S4. Comparison of 10-sample DGE to 44-sample DGE. (a) Self-comparisons (e.g. MaLTE 10-sample DGE to MaLTE 44-sample DGE, with five and 22 samples in each tissue, respectively). Forty-four-sample DGE results are treated as putative true differential expressions at the indicated (top right or each plot) false discover rates (FDRs). FDRs were computed using the q-value method (Storey and Tibshirani 2003). Comparisons are made using the Jaccard index. (b) Comparison of each method to 44-sample DGE using RNA-Seq. Figure S5. Transcript isoform expression estimates. Densities of (a) Pearson and (b) Spearman cross-sample correlations for transcript isoform expression estimates obtained using MaLTE. Filtered data corresponds to transcript isoforms with r OOB>0. Figure S6. Box plots of slopes β computed by linearly regressing RNA-Seq against array method. Dotted red line indicates unit gradient. Each box plot represents 12 slopes for brain samples. RNA-Seq expression is restricted to RPKM between one and 1000 because of high uncertainty at low values and few genes at high values representing 78% of genes quantified. Using all genes results in the same medians but wider variance in slopes due to outlying genes. Figure S7. Estimation of tissue mixture proportions. (a) MaLTE, (b) median-polish (RMA), and (c) PLIER. Each plot shows comparisons of true and estimated proportions with key statistics indicated in the legends.

The development of high-throughput genomics technologies has contributed substantially to the understanding of gene expression regulation. With the growing appreciation of the importance of alternative splicing, quantitative techniques have had to keep in step with the demand for an accurate and high resolution view of the transcriptome. In this thesis, we use results and methods from quantitative genomics to explore how gene regulation may be modified in development and disease. Precise regulation of gene expression timing can be critical for some biological processes. This is particularly the case for genes with oscillating patterns of expression. Oscillations can be brought about through negative feedback loops, with a delay between gene activation and negative autoregulation. The time required for gene transcription contributes to the delay in gene activation and, thus, the intron content of genes involved in negative autoregulatory loops can be functionally significant. An example of this occurs in Hes7, in which oscillation is coupled to the formation of segmental body plans during animal development. To identify further examples of genes in which the transcriptional delay introduced by introns may be functionally significant, we carried out a search for genes with conserved intron content across a diverse panel of 19 mammals and found that the set of genes with the most extreme conservation was enriched for genes involved in embryonic development. We found that these genes had both fewer insertions and deletions as well as a balance between the cumulative insertions and deletions, suggesting that selection functions to prevent indels in these introns and to balance the impact of insertions and deletions. There has been considerable success in mapping local (cis) variants associated with phenotypes such as disease. Many cis-acting variants that cause disease disrupt splicing. However, mapping distant (trans) acting variants that affect splicing is a more formidable task. By exploiting high-density transcriptome microarrays, we show that a mutation in the splicing factor PRPF8, causally associated with retinitis pigmentosa, is a trans-acting splicing variant. We estimate that up to 20% of all exons are mis-spliced through higher exon inclusion in affected individuals. Characteristics of affected exons suggest that they tend to be spliced co-transcriptionally and via the exon-defined splicing pathway. The importance of gene expression microarrays in quantitative genomics has led to the development of numerous algorithms to estimate gene expression from raw microarray intensities. But microarrays have several shortcomings relative to more recently developed sequencing-based methods for measuring gene expression. We exploited the benefits of quantitative transcriptome sequencing (RNA-Seq) by using a statistical learning approach to obtain better expression estimates from arrays, based on a high-quality dataset for which both microarray and RNA-Seq data are available. Our analyses show that this approach compares favourably to existing algorithms for microarray analysis, with the added advantage of providing estimates of the abundance of individual transcript isoforms on an absolute scale.