@article {49608, title = {RNA-sequencing of the brain transcriptome implicates dysregulation of neuroplasticity, circadian rhythms and GTPase binding in bipolar disorder.}, volume = {19}, year = {2014}, month = {2014 Nov}, pages = {1179-85}, abstract = {

RNA-sequencing (RNA-seq) is a powerful technique to investigate the complexity of gene expression in the human brain. We used RNA-seq to survey the brain transcriptome in high-quality postmortem dorsolateral prefrontal cortex from 11 individuals diagnosed with bipolar disorder (BD) and from 11 age- and gender-matched controls. Deep sequencing was performed, with over 350 million reads per specimen. At a false discovery rate of <5\%, we detected five differentially expressed (DE) genes and 12 DE transcripts, most of which have not been previously implicated in BD. Among these, Prominin 1/CD133 and ATP-binding cassette-sub-family G-member2 (ABCG2) have important roles in neuroplasticity. We also show for the first time differential expression of long noncoding RNAs (lncRNAs) in BD. DE transcripts include those of serine/arginine-rich splicing factor 5 (SRSF5) and regulatory factor X4 (RFX4), which along with lncRNAs have a role in mammalian circadian rhythms. The DE genes were significantly enriched for several Gene Ontology categories. Of these, genes involved with GTPase binding were also enriched for BD-associated SNPs from previous genome-wide association studies, suggesting that differential expression of these genes is not simply a consequence of BD or its treatment. Many of these findings were replicated by microarray in an independent sample of 60 cases and controls. These results highlight common pathways for inherited and non-inherited influences on disease risk that may constitute good targets for novel therapies.

}, keywords = {Adult, Aged, Bipolar Disorder, Circadian Rhythm, Female, Genome-Wide Association Study, GTP Phosphohydrolases, HUMANS, Male, Meta-Analysis as Topic, Microarray Analysis, Middle Aged, Neuronal Plasticity, Polymerase Chain Reaction, Prefrontal Cortex, Principal Component Analysis, Sequence Analysis, RNA, Transcriptome, Young Adult}, issn = {1476-5578}, doi = {10.1038/mp.2013.170}, author = {Akula, N and Barb, J and Jiang, X and Wendland, J R and Choi, K H and Sen, S K and Hou, L and Chen, D T W and Laje, G and Johnson, K and Lipska, B K and Kleinman, J E and Corrada-Bravo, H and Detera-Wadleigh, S and Munson, P J and McMahon, F J} } @article {49572, title = {Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms.}, volume = {32}, year = {2014}, month = {2014 May}, pages = {462-4}, abstract = {

We introduce Sailfish, a computational method for quantifying the abundance of previously annotated RNA isoforms from RNA-seq data. Because Sailfish entirely avoids mapping reads, a time-consuming step in all current methods, it provides quantification estimates much faster than do existing approaches (typically 20 times faster) without loss of accuracy. By facilitating frequent reanalysis of data and reducing the need to optimize parameters, Sailfish exemplifies the potential of lightweight algorithms for efficiently processing sequencing reads.

}, keywords = {algorithms, Brain Chemistry, Computational Biology, HUMANS, Models, Biological, RNA Isoforms, Sequence Analysis, RNA, software}, issn = {1546-1696}, doi = {10.1038/nbt.2862}, author = {Patro, Rob and Mount, Stephen M and Kingsford, Carl} } @article {49681, title = {Spliceosomal small nuclear RNA genes in 11 insect genomes.}, journal = {RNA}, volume = {13}, year = {2007}, month = {2007 Jan}, pages = {5-14}, abstract = {

The removal of introns from the primary transcripts of protein-coding genes is accomplished by the spliceosome, a large macromolecular complex of which small nuclear RNAs (snRNAs) are crucial components. Following the recent sequencing of the honeybee (Apis mellifera) genome, we used various computational methods, ranging from sequence similarity search to RNA secondary structure prediction, to search for putative snRNA genes (including their promoters) and to examine their pattern of conservation among 11 available insect genomes (A. mellifera, Tribolium castaneum, Bombyx mori, Anopheles gambiae, Aedes aegypti, and six Drosophila species). We identified candidates for all nine spliceosomal snRNA genes in all the analyzed genomes. All the species contain a similar number of snRNA genes, with the exception of A. aegypti, whose genome contains more U1, U2, and U5 genes, and A. mellifera, whose genome contains fewer U2 and U5 genes. We found that snRNA genes are generally more closely related to homologs within the same genus than to those in other genera. Promoter regions for all spliceosomal snRNA genes within each insect species share similar sequence motifs that are likely to correspond to the PSEA (proximal sequence element A), the binding site for snRNA activating protein complex, but these promoter elements vary in sequence among the five insect families surveyed here. In contrast to the other insect species investigated, Dipteran genomes are characterized by a rapid evolution (or loss) of components of the U12 spliceosome and a striking loss of U12-type introns.

}, keywords = {Animals, Base Sequence, Bees, Computational Biology, Diptera, Evolution, Molecular, Genes, Insect, Genome, Insect, Molecular Sequence Data, Nucleic Acid Conformation, Phylogeny, Promoter Regions, Genetic, RNA Splicing, RNA, Small Nuclear, Sequence Analysis, RNA, Spliceosomes}, issn = {1355-8382}, doi = {10.1261/rna.259207}, author = {Mount, Stephen M and Gotea, Valer and Lin, Chiao-Feng and Hernandez, Kristina and Makalowski, Wojciech} }