Count data derived from high-throughput deoxy-ribonucliec acid (DNA) sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. Commonly used count data normalization approaches like library size scaling/rarefaction/subsampling cannot correct for compositional or any other relevant technical bias that is uncorrelated with library size. We demonstrate that existing techniques for estimating compositional bias fail with sparse metagenomic 16S count data and propose an empirical Bayes normalization approach to overcome this problem. In addition, we clarify the assumptions underlying frequently used scaling normalization methods in light of compositional bias, including scaling methods that were not designed directly to address it. Compositional bias, induced by the sequencing machine, confounds inferences of absolute abundances. We present a normalization technique for compositional bias correction in sparse sequencing count data, and demonstrate its improved performance in metagenomic 16s survey data. Based on the distribution of technical bias estimates arising from several publicly available large scale 16s count datasets, we argue that detailed experiments specifically addressing the influence of compositional bias in metagenomics are needed.

Machine learning tool TEEBoT predicts an individual’s tissue-specific gene expression from his/her blood transcriptome.

Genomes of higher eukaryotes have surprisingly long first introns and in some cases, the first introns have been shown to have higher conservation relative to other introns. However, the functional relevance of conserved regions in the first introns is poorly understood. Leveraging the recent ENCODE data, here we assess potential regulatory roles of conserved regions in the first intron of human genes. We first show that relative to other downstream introns, the first introns are enriched for blocks of highly conserved sequences. We also found that the first introns are enriched for several chromatin marks indicative of active regulatory regions and this enrichment of regulatory marks is correlated with enrichment of conserved blocks in the first intron; the enrichments of conservation and regulatory marks in first intron are not entirely explained by a general, albeit variable, bias for certain marks toward the 5’ end of introns. Interestingly, conservation as well as proportions of active regulatory chromatin marks in the first intron of a gene correlates positively with the numbers of exons in the gene but the correlation is significantly weakened in second introns and negligible beyond the second intron. The first intron conservation is also positively correlated with the gene’s expression level in several human tissues. Finally, a gene-wise analysis shows significant enrichments of active chromatin marks in conserved regions of first introns, relative to the conserved regions in other introns of the same gene. Taken together, our analyses strongly suggest that first introns are enriched for active transcriptional regulatory signals under purifying selection.

It is well known that young proteins tend to experience weaker purifying selection and evolve more quickly than old proteins. Here, we show that, in addition, young proteins tend to experience more variable selection pressures over time than old proteins. We demonstrate this pattern in three independent taxonomic groups: yeast, Drosophila, and mammals. The increased variability of selection pressures on young proteins is highly significant even after controlling for the fact that young proteins are typically shorter and experience weaker purifying selection than old proteins. The majority of our results are consistent with the hypothesis that the function of a young gene tends to change over time more readily than that of an old gene. At the same time, our results may be caused in part by young genes that serve constant functions over time, but nevertheless appear to evolve under changing selection pressures due to depletion of adaptive mutations. In either case, our results imply that the evolution of a protein-coding sequence is partly determined by its age and origin, and not only by the phenotypic properties of the encoded protein. We discuss, via specific examples, the consequences of these findings for understanding of the sources of evolutionary novelty.