CADBURE: A generic tool to evaluate the performance of spliced aligners on RNA-Seq data.

The fundamental task in RNA-Seq-based transcriptome analysis is alignment of millions of short reads to the reference genome or transcriptome. Choosing the right tool for the dataset in hand from many existent RNA-Seq alignment packages remains a critical challenge for downstream analysis. To facilitate this choice, we designed a novel tool for comparing alignment results of user data based on the relative reliability of uniquely aligned reads (CADBURE). CADBURE can easily evaluate different aligners, or different parameter sets using the same aligner, and selects the best alignment result for any RNA-Seq dataset. Strengths of CADBURE include the ability to compare alignment results without the need for synthetic data such as simulated genomes, alignment regeneration and randomly subsampled datasets. The benefit of a CADBURE selected alignment result was supported by differentially expressed gene (DEG) analysis. We demonstrated that the use of CADBURE to select the best alignment from a number of different alignment results could change the number of DEGs by as much as 10%. In particular, the CADBURE selected alignment result favors fewer false positives in the DEG analysis. We also verified differential expression of eighteen genes with RT-qPCR validation experiments. CADBURE is an open source tool (http://cadbure.sourceforge.net/).

Comparative transcriptome analysis of epithelial and fiber cells in newborn mouse lenses with RNA sequencing.

PURPOSE: The ocular lens contains only two cell types: epithelial cells and fiber cells. The epithelial cells lining the anterior hemisphere have the capacity to continuously proliferate and differentiate into lens fiber cells that make up the large proportion of the lens mass. To understand the transcriptional changes that take place during the differentiation process, high-throughput RNA-Seq of newborn mouse lens epithelial cells and lens fiber cells was conducted to comprehensively compare the transcriptomes of these two cell types. METHODS: RNA from three biologic replicate samples of epithelial and fiber cells from newborn FVB/N mouse lenses was isolated and sequenced to yield more than 24 million reads per sample. Sequence reads that passed quality filtering were mapped to the reference genome using Genomic Short-read Nucleotide Alignment Program (GSNAP). Transcript abundance and differential gene expression were estimated using the Cufflinks and DESeq packages, respectively. Gene Ontology enrichment was analyzed using GOseq. RNA-Seq results were compared with previously published microarray data. The differential expression of several biologically important genes was confirmed using reverse transcription (RT)-quantitative PCR (qPCR). RESULTS: Here, we present the first application of RNA-Seq to understand the transcriptional changes underlying the differentiation of epithelial cells into fiber cells in the newborn mouse lens. In total, 6,022 protein-coding genes exhibited differential expression between lens epithelial cells and lens fiber cells. To our knowledge, this is the first study identifying the expression of 254 long intergenic non-coding RNAs (lincRNAs) in the lens, of which 86 lincRNAs displayed differential expression between the two cell types. We found that RNA-Seq identified more differentially expressed genes and correlated with RT-qPCR quantification better than previously published microarray data. Gene Ontology analysis showed that genes upregulated in the epithelial cells were enriched for extracellular matrix production, cell division, migration, protein kinase activity, growth factor binding, and calcium ion binding. Genes upregulated in the fiber cells were enriched for proteosome complexes, unfolded protein responses, phosphatase activity, and ubiquitin binding. Differentially expressed genes involved in several important signaling pathways, lens structural components, organelle loss, and denucleation were also highlighted to provide insights into lens development and lens fiber differentiation. CONCLUSIONS: RNA-Seq analysis provided a comprehensive view of the relative abundance and differential expression of protein-coding and non-coding transcripts from lens epithelial cells and lens fiber cells. This information provides a valuable resource for studying lens development, nuclear degradation, and organelle loss during fiber differentiation, and associated diseases.

Bioinformatics analysis of alternative polyadenylation in green alga Chlamydomonas reinhardtii using transcriptome sequences from three different sequencing platforms.

Messenger RNA 3'-end formation is an essential posttranscriptional processing step for most eukaryotic genes. Different from plants and animals where AAUAAA and its variants routinely are found as the main poly(A) signal, Chlamydomonas reinhardtii uses UGUAA as the major poly(A) signal. The advance of sequencing technology provides an enormous amount of sequencing data for us to explore the variations of poly(A) signals, alternative polyadenylation (APA), and its relationship with splicing in this algal species. Through genome-wide analysis of poly(A) sites in C. reinhardtii, we identified a large number of poly(A) sites: 21,041 from Sanger expressed sequence tags, 88,184 from 454, and 195,266 from Illumina sequence reads. In comparison with previous collections, more new poly(A) sites are found in coding sequences and intron and intergenic regions by deep-sequencing. Interestingly, G-rich signals are particularly abundant in intron and intergenic regions. The prevalence of different poly(A) signals between coding sequences and a 3'-untranslated region implies potentially different polyadenylation mechanisms. Our data suggest that the APA occurs in about 68% of C. reinhardtii genes. Using Gene Ontolgy analysis, we found most of the APA genes are involved in RNA regulation and metabolic process, protein synthesis, hydrolase, and ligase activities. Moreover, intronic poly(A) sites are more abundant in constitutively spliced introns than retained introns, suggesting an interplay between polyadenylation and splicing. Our results support that APA, as in higher eukaryotes, may play significant roles in increasing transcriptome diversity and gene expression regulation in this algal species. Our datasets also provide useful information for accurate annotation of transcript ends in C. reinhardtii.