Exploring the gonad transcriptome of two extreme male pigs with RNA-seq

Background: Although RNA-seq greatly advances our understanding of complex transcriptome landscapes, such as those found in mammals, complete RNA-seq studies in livestock and in particular in the pig are still lacking. Here, we used high-throughput RNA sequencing to gain insight into the characterization of the poly-A RNA fraction expressed in pig male gonads. An expression analysis comparing different mapping approaches and detection of allele specific expression is also discussed in this study. Results: By sequencing testicle mRNA of two phenotypically extreme pigs, one Iberian and one Large White, we identified hundreds of unannotated protein-coding genes (PcGs) in intergenic regions, some of them presenting orthology with closely related species. Interestingly, we also detected 2047 putative long non-coding RNA (lncRNA), including 469 with human homologues. Two methods, DEGseq and Cufflinks, were used for analyzing expression. DEGseq identified 15% less expressed genes than Cufflinks, because DEGseq utilizes only unambiguously mapped reads. Moreover, a large fraction of the transcriptome is made up of transposable elements (14500 elements encountered), as has been reported in previous studies. Gene expression results between microarray and RNA-seq technologies were relatively well correlated (r = 0.71 across individuals). Differentially expressed genes between Large White and Iberian showed a significant overrepresentation of gamete production and lipid metabolism gene ontology categories. Finally, allelic imbalance was detected in ~ 4% of heterozygous sites. Conclusions: RNA-seq is a powerful tool to gain insight into complex transcriptomes. In addition to uncovering many unnanotated genes, our study allowed us to determine that a considerable fraction is made up of long non-coding transcripts and transposable elements. Their biological roles remain to be determined in future studies. In terms of differences in expression between Large White and Iberian pigs, these were largest for genes involved in spermatogenesis and lipid metabolism, which is consistent with phenotypic extreme differences in prolificacy and fat deposition between these two breeds

Directed acyclic graph kernels for structural RNA analysis

<p>Abstract</p> <p>Background</p> <p>Recent discoveries of a large variety of important roles for non-coding RNAs (ncRNAs) have been reported by numerous researchers. In order to analyze ncRNAs by kernel methods including support vector machines, we propose stem kernels as an extension of string kernels for measuring the similarities between two RNA sequences from the viewpoint of secondary structures. However, applying stem kernels directly to large data sets of ncRNAs is impractical due to their computational complexity.</p> <p>Results</p> <p>We have developed a new technique based on directed acyclic graphs (DAGs) derived from base-pairing probability matrices of RNA sequences that significantly increases the computation speed of stem kernels. Furthermore, we propose profile-profile stem kernels for multiple alignments of RNA sequences which utilize base-pairing probability matrices for multiple alignments instead of those for individual sequences. Our kernels outperformed the existing methods with respect to the detection of known ncRNAs and kernel hierarchical clustering.</p> <p>Conclusion</p> <p>Stem kernels can be utilized as a reliable similarity measure of structural RNAs, and can be used in various kernel-based applications.</p

Accelerated Profile HMM Searches

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the “multiple segment Viterbi” (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call “sparse rescaling”. These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches

Non-Coding RNA Prediction and Verification in Saccharomyces cerevisiae

Non-coding RNA (ncRNA) play an important and varied role in cellular function. A significant amount of research has been devoted to computational prediction of these genes from genomic sequence, but the ability to do so has remained elusive due to a lack of apparent genomic features. In this work, thermodynamic stability of ncRNA structural elements, as summarized in a Z-score, is used to predict ncRNA in the yeast Saccharomyces cerevisiae. This analysis was coupled with comparative genomics to search for ncRNA genes on chromosome six of S. cerevisiae and S. bayanus. Sets of positive and negative control genes were evaluated to determine the efficacy of thermodynamic stability for discriminating ncRNA from background sequence. The effect of window sizes and step sizes on the sensitivity of ncRNA identification was also explored. Non-coding RNA gene candidates, common to both S. cerevisiae and S. bayanus, were verified using northern blot analysis, rapid amplification of cDNA ends (RACE), and publicly available cDNA library data. Four ncRNA transcripts are well supported by experimental data (RUF10, RUF11, RUF12, RUF13), while one additional putative ncRNA transcript is well supported but the data are not entirely conclusive. Six candidates appear to be structural elements in 5′ or 3′ untranslated regions of annotated protein-coding genes. This work shows that thermodynamic stability, coupled with comparative genomics, can be used to predict ncRNA with significant structural elements

A comparative genome-wide study of ncRNAs in trypanosomatids

<p>Abstract</p> <p>Background</p> <p>Recent studies have provided extensive evidence for multitudes of non-coding RNA (ncRNA) transcripts in a wide range of eukaryotic genomes. ncRNAs are emerging as key players in multiple layers of cellular regulation. With the availability of many whole genome sequences, comparative analysis has become a powerful tool to identify ncRNA molecules. In this study, we performed a systematic genome-wide in silico screen to search for novel small ncRNAs in the genome of <it>Trypanosoma brucei </it>using techniques of comparative genomics.</p> <p>Results</p> <p>In this study, we identified by comparative genomics, and validated by experimental analysis several novel ncRNAs that are conserved across multiple trypanosomatid genomes. When tested on known ncRNAs, our procedure was capable of finding almost half of the known repertoire through homology over six genomes, and about two-thirds of the known sequences were found in at least four genomes. After filtering, 72 conserved unannotated sequences in at least four genomes were found, 29 of which, ranging in size from 30 to 392 nts, were conserved in all six genomes. Fifty of the 72 candidates in the final set were chosen for experimental validation. Eighteen of the 50 (36%) were shown to be expressed, and for 11 of them a distinct expression product was detected, suggesting that they are short ncRNAs. Using functional experimental assays, five of the candidates were shown to be novel H/ACA and C/D snoRNAs; these included three sequences that appear as singletons in the genome, unlike previously identified snoRNA molecules that are found in clusters. The other candidates appear to be novel ncRNA molecules, and their function is, as yet, unknown.</p> <p>Conclusions</p> <p>Using comparative genomic techniques, we predicted 72 sequences as ncRNA candidates in <it>T. brucei</it>. The expression of 50 candidates was tested in laboratory experiments. This resulted in the discovery of 11 novel short ncRNAs in procyclic stage <it>T. brucei</it>, which have homologues in the other trypansomatids. A few of these molecules are snoRNAs, but most of them are novel ncRNA molecules. Based on this study, our analysis suggests that the total number of ncRNAs in trypanosomatids is in the range of several hundred.</p

Mining of miRNAs and potential targets from gene oriented clusters of transcripts sequences of the anti-malarial plant, Artemisia annua

miRNAs involved in the biosynthesis of artemisinin, an anti-malarial compound form the plant Artemisia annua, have been identified using computational approaches to find conserved pre-miRNAs in available A. annua UniGene collections. Eleven pre-miRNAs were found from nine families. Targets predicted for these miRNAs were mainly transcription factors for conserved miRNAs. No target genes involved in artemisinin biosynthesis were found. However, miR390 was predicted to target a gene involved in the trichome development, which is the site of synthesis of artemisinin and could be a candidate for genetic transformation aiming to increase the content of artemisinin. Phylogenetic analyses were carried out to determinate the relation between A. annua and other plant pre-miRNAs: the pre-miRNA-based phylogenetic trees failed to correspond to known phylogenies, suggesting that pre-miRNA primary sequences may be too variable to accurately predict phylogenetic relations

A Strand-Specific RNA–Seq Analysis of the Transcriptome of the Typhoid Bacillus Salmonella Typhi

High-density, strand-specific cDNA sequencing (ssRNA–seq) was used to analyze the transcriptome of Salmonella enterica serovar Typhi (S. Typhi). By mapping sequence data to the entire S. Typhi genome, we analyzed the transcriptome in a strand-specific manner and further defined transcribed regions encoded within prophages, pseudogenes, previously un-annotated, and 3′- or 5′-untranslated regions (UTR). An additional 40 novel candidate non-coding RNAs were identified beyond those previously annotated. Proteomic analysis was combined with transcriptome data to confirm and refine the annotation of a number of hpothetical genes. ssRNA–seq was also combined with microarray and proteome analysis to further define the S. Typhi OmpR regulon and identify novel OmpR regulated transcripts. Thus, ssRNA–seq provides a novel and powerful approach to the characterization of the bacterial transcriptome