Search bioRxivSearch

Biology subjects

Thomas R Gingeras

Publications and source records attributed to Thomas R Gingeras.

2 recordsLinked to original sources

Enhanced Transcriptome Maps from Multiple Mouse Tissues Reveal Evolutionary Constraint in Gene Expression for Thousands of Genes

We characterized by RNA-seq the transcriptional profiles of a large and heterogeneous collection of mouse tissues, augmenting the mouse transcriptome with thousands of novel transcript candidates. Comparison with transcriptome profiles obtained in human cell lines reveals substantial conservation of transcriptional programs, and uncovers a distinct class of genes with levels of expression across cell types and species, that have been constrained early in vertebrate evolution. This core set of genes capture a substantial and constant fraction of the transcriptional output of mammalian cells, and participates in basic functional and structural housekeeping processes common to all cell types. Perturbation of these constrained genes is associated with significant phenotypes including embryonic lethality and cancer. Evolutionary constraint in gene expression levels is not reflected in the conservation of the genomic sequences, but is associated with strong and conserved epigenetic marking, as well as to a characteristic post-transcriptional regulatory program in which sub-cellular localization and alternative splicing play comparatively large roles.

Bioinformatics

Comment on “TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions” by Kim et al.

In the recent paper [1] (thereafter referred to as \"TopHat2paper\") the accuracy of TopHat2 was compared to other RNA-seq aligners. In this comment we re-examine most important analyses from the TopHat2paper and identify several deficiencies that significantly diminished performance of some of the aligners, including incorrect choice of mapping parameters, unfair comparison metrics, and unrealistic simulated data. Using STAR [2] as an exemplar, we demonstrate that correcting these deficiencies makes its accuracy equal or better than that of TopHat2. Furthermore, this exercise highlighted some serious issues with the TopHat2 algorithms, such as poor recall of alignments with a moderate (>3) number of mismatches, low sensitivity and high false discovery rate for splice junction detection, loss of precision for the realignment algorithm, and large number of false chimeric alignments. ...

Bioinformatics