RNA-seq: from technology to biology

De novo sequencing and transcriptome analysis of venom glands of endoparasitoid Aenasius arizonensis (Girault) (=Aenasius bambawalei Hayat) (Hymenoptera, Encyrtidae)

Aenasius bambawalei Hayat (Encyrtidae: Hymenoptera) has been synonymized with Aenasius arizonensis (Girault) is a small, newly discovered endoparasitoid of the cotton mealybug Phenacoccuss solenopsis Tinsley (Pseudococcidae: Hemiptera), which completes its life cycle inside the body of its host and it is a potential insect control tool. Despite the acquired knowledge regarding host-parasitoid interaction, little information is available on the factors of parasitoid origin able to modulate mealybug physiology. The components of A. arizonensis venom have not been well studied but venom from other parasitoids and wasps contain biologically active proteins that have potential applications in pest management or may be of medicinal importance. To provide an insight into the transcripts expressed in the venom gland of A. arizonensis, a transcriptomic database was developed utilizing high throughput RNA sequencing approaches to analyze the genes expressed in venom glands of this endoparasitic wasp. The resulting A. arizonensis RNA sequences were assembled de-novo with contigs then blasted against the NCBI non-redundant sequence database. Contigs which matched database sequences were mostly homologous to genes from hymenopteran parasitoids such as Nasonia vitripennis, Copidosoma floridanum, Fopius arsenus and Pteromalas puparium. Further analysis of the A. arizonensis database was then performed which focused on selected genes encoding proteins potentially involved in host developmental arrest, disrupting the host immune system, host paralysis, and transcripts that support these functions. Sequenced mRNAS predicted to encode full length ORFs of Calreticulin, Serine Protease Precursor and Arginine kinase proteins were identified and the tissue specific expression of these putative venom genes was analyzed by RT-PCR. In addition, results also demonstrate that de novo transcriptome assembly allows useful venom gene expression analysis in a species lacking a genome sequence database and may provide useful information for devising control tools for insect pests and other applications.

De Novo Transcriptome Assembly and Annotation of the Leaves and Callus of Cyclocarya Paliurus (Bata1) Iljinskaja

Cyclocarya Paliurus (Bata1) Iljinskaja contains various bioactive secondary metabolites especially in leaves, such as triterpenes, flavonoids, polysaccharides and alkaloids, and its leaves are widely used as an hyperglycemic tea in China. In the present paper, we sequenced the transcriptome of the leaves and callus of Cyclocarya Paliurus using Illumina Hiseq 4000 platform. After sequencing and de novo assembly, a total of 65,654 unigenes were generated with an N50 length of 1,244bp. Among them, 35,041 (53.37%) unigenes were annotated in NCBI Non-Redundant database, 19,453 (29.63%) unigenes were classified into Gene Ontology (GO) database, and 7,259 (11.06%) unigenes were assigned to Clusters of Orthologous Group (COG) categories. Furthermore, 11,697 (17.81%) unigenes were mapped onto 335 pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG), among which 1,312 unigenes were identified to be involved in biosynthesis of secondary metabolites. In addition, a total of 11,247 putative simple sequence repeats (SSRs) were detected. This transcriptome dataset provides a comprehensive sequence resource for gene expression profiling, genetic diversity, evolution and further molecular genetics research on Cyclocarya Paliurus.

Biological Network Edit Distance

Interactions among biological entities contain more information than purely the similarities between the entities. For example, interactions between genes, and gene products, can be more informative than the sequence similarities of the genes involved. However, the study of biological networks and their evolution in particular is still in its infancy. Simplified theoretical models of the development of biological networks from a starting state exist, but the problem of finding a distance between existing biological networks, with an unknown history, has seen less research. Metrics for network distance can also be used to measure the fit between theoretically derived networks and their real-world counterpart. In this article, we present a useful model of biological network distance and demonstrate an implementation using simulated gene regulatory networks. We compared our method with existing methods for network alignment and showed that we are much better able to identify evolutionary changes in biological networks. In particular, we can recover the evolutionary trees that describe the relationship between these networks.

Integrative analysis of transcriptomics and proteomics of skeletal muscles of the Chinese indigenous Shaziling pig compared with the Yorkshire breed

Background

The Shaziling pig (Sus scrofa) is a well-known indigenous breed in China. One of its main advantages over European breeds is its high meat quality. However, little genetic information is available for the Shaziling pig. To screen for differentially expressed genes and proteins that might be responsible for the meat quality, the longissimus dorsi muscles from Shaziling and Yorkshire pig breeds were investigated using an integrative analysis of transcriptomics and proteomics, involving high-throughput sequencing, the two-dimensional gel electrophoresis, and mass spectrometry.

Results

Sequencing produced 79,320 unigenes by de novo assembly, and 488 differentially expressed genes in the longissimus dorsi muscle of Shaziling pig compared with the Yorkshire breed were identified. Gene Ontology term enrichment of biological functions and Kyoto Encyclopedia of Genes and Genomes analysis showed that the gene products were mainly involved in metabolism, protein binding, and regulation of skeletal muscle development. At the protein level, 23 differentially expressed proteins were identified, which were potentially associated with fatty acid metabolism, the glycolytic pathway, and skeletal muscle growth. Eight differentially expressed genes were confirmed by real-time PCR. These results give an insight into the mechanisms underlying the formation of skeletal muscle in the Shaziling pig.

Conclusions

Certain differentially expressed genes and proteins are involved in fatty acid metabolism, intramuscular fat deposition, and skeletal muscle growth in the Shaziling pig. These results provide candidate genes for improving meat quality and will promote further transcriptomic research in Shaziling pigs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0389-y) contains supplementary material, which is available to authorized users.

Suppression Subtractive Hybridization Versus Next-Generation Sequencing in Plant Genetic Engineering: Challenges and Perspectives

Suppression subtractive hybridization (SSH) is an effective method to identify different genes with different expression levels involved in a variety of biological processes. This method has often been used to study molecular mechanisms of plants in complex relationships with different pathogens and a variety of biotic stresses. Compared to other techniques used in gene expression profiling, SSH needs relatively smaller amounts of the initial materials, with lower costs, and fewer false positives present within the results. Extraction of total RNA from plant species rich in phenolic compounds, carbohydrates, and polysaccharides that easily bind to nucleic acids through cellular mechanisms is difficult and needs to be considered. Remarkable advancement has been achieved in the next-generation sequencing (NGS) field. As a result of progress within fields related to molecular chemistry and biology as well as specialized engineering, parallelization in the sequencing reaction has exceptionally enhanced the overall read number of generated sequences per run. Currently available sequencing platforms support an earlier unparalleled view directly into complex mixes associated with RNA in addition to DNA samples. NGS technology has demonstrated the ability to sequence DNA with remarkable swiftness, therefore allowing previously unthinkable scientific accomplishments along with novel biological purposes. However, the massive amounts of data generated by NGS impose a substantial challenge with regard to data safe-keeping and analysis. This review examines some simple but vital points involved in preparing the initial material for SSH and introduces this method as well as its associated applications to detect different novel genes from different plant species. This review evaluates general concepts, basic applications, plus the probable results of NGS technology in genomics, with unique mention of feasible potential tools as well as bioinformatics.

Effects of Sex and Notch Signaling on the Osteocyte Cell Pool

Osteocytes play a fundamental role in mechanotransduction and skeletal remodeling. Sex is a determinant of skeletal structure, and female C57BL/6J mice have increased osteoblast number in cancellous bone when compared to male mice. Activation of Notch in the skeleton causes profound cell-context dependent changes in skeletal physiology. To determine the impact of sex and of Notch signaling on the osteocyte cell pool, we analyzed cancellous and cortical bone of 1-6-month-old C57BL/6J or 129SvJ/C57BL/6J mice and determined the osteocyte number/area. There was an age-dependent decline in osteocyte number in cancellous bone of male but not female mice, so that 6-month-old female mice had a greater number of osteocytes than male littermates. Although differences between male and female mice were modest, female mice had ∼10-15% greater number of osteocytes/area. RNA sequence analysis of osteocyte-rich preparations did not reveal differences between sexes in the expression of genes known to influence bone homeostasis. Neither the activation of Notch1 nor the concomitant inactivation of Notch1 and Notch2 in Osterix (Sp7) or Dentin matrix protein 1 (Dmp1) expressing cells had a pronounced and consistent effect on cancellous or cortical bone osteocyte number in either sex. Moreover, inactivation of Notch1 and Notch2 in Dmp1 expressing cells did not influence the bone loss in a muscle immobilization model of skeletal unloading. In conclusion, cancellous bone osteocytes decline with age in male mice, cortical osteocytes are influenced by sex in younger mice, but osteocyte cell density is not affected substantially by Notch signaling. J. Cell. Physiol. 232: 363-370, 2017. © 2016 Wiley Periodicals, Inc.

Replicates, Read Numbers, and Other Important Experimental Design Considerations for Microbial RNA-seq Identified Using Bacillus thuringiensis Datasets

RNA-seq is being used increasingly for gene expression studies and it is revolutionizing the fields of genomics and transcriptomics. However, the field of RNA-seq analysis is still evolving. Therefore, we specifically designed this study to contain large numbers of reads and four biological replicates per condition so we could alter these parameters and assess their impact on differential expression results. Bacillus thuringiensis strains ATCC10792 and CT43 were grown in two Luria broth medium lots on four dates and transcriptomics data were generated using one lane of sequence output from an Illumina HiSeq2000 instrument for each of the 32 samples, which were then analyzed using DESeq2. Genome coverages across samples ranged from 87 to 465X with medium lots and culture dates identified as major variation sources. Significantly differentially expressed genes (5% FDR, two-fold change) were detected for cultures grown using different medium lots and between different dates. The highly differentially expressed iron acquisition and metabolism genes, were a likely consequence of differing amounts of iron in the two media lots. Indeed, in this study RNA-seq was a tool for predictive biology since we hypothesized and confirmed the two LB medium lots had different iron contents (~two-fold difference). This study shows that the noise in data can be controlled and minimized with appropriate experimental design and by having the appropriate number of replicates and reads for the system being studied. We outline parameters for an efficient and cost effective microbial transcriptomics study.

An optimized protocol for generation and analysis of Ion Proton sequencing reads for RNA-Seq

BACKGROUND

Previous studies compared running cost, time and other performance measures of popular sequencing platforms. However, comprehensive assessment of library construction and analysis protocols for Proton sequencing platform remains unexplored. Unlike Illumina sequencing platforms, Proton reads are heterogeneous in length and quality. When sequencing data from different platforms are combined, this can result in reads with various read length. Whether the performance of the commonly used software for handling such kind of data is satisfactory is unknown.

RESULTS

By using universal human reference RNA as the initial material, RNaseIII and chemical fragmentation methods in library construction showed similar result in gene and junction discovery number and expression level estimated accuracy. In contrast, sequencing quality, read length and the choice of software affected mapping rate to a much larger extent. Unspliced aligner TMAP attained the highest mapping rate (97.27 % to genome, 86.46 % to transcriptome), though 47.83 % of mapped reads were clipped. Long reads could paradoxically reduce mapping in junctions. With reference annotation guide, the mapping rate of TopHat2 significantly increased from 75.79 to 92.09 %, especially for long (>150 bp) reads. Sailfish, a k-mer based gene expression quantifier attained highly consistent results with that of TaqMan array and highest sensitivity.

CONCLUSION

We provided for the first time, the reference statistics of library preparation methods, gene detection and quantification and junction discovery for RNA-Seq by the Ion Proton platform. Chemical fragmentation performed equally well with the enzyme-based one. The optimal Ion Proton sequencing options and analysis software have been evaluated.

Gene Expression Changes during the Gummosis Development of Peach Shoots in Response to Lasiodiplodia theobromae Infection Using RNA-Seq

Lasiodiplodia theobromae is a causal agent of peach (Prunus persica L.) tree gummosis, a serious disease affecting peach cultivation and production. However, the molecular mechanism underlying the pathogenesis remains unclear. RNA-Seq was performed to investigate gene expression in peach shoots inoculated or mock-inoculated with L. theobromae. A total of 20772 genes were detected in eight samples; 4231, 3750, 3453, and 3612 differentially expressed genes were identified at 12, 24, 48, and 60 h after inoculation, respectively. Furthermore, 920 differentially co-expressed genes (515 upregulated and 405 downregulated) were found, respectively. Gene ontology annotation revealed that phenylpropanoid biosynthesis and metabolism, uridine diphosphate-glucosyltransferase activity, and photosynthesis were the most differentially regulated processes during gummosis development. Significant differences were also found in the expression of genes involved in glycometabolism and in ethylene and jasmonic acid biosynthesis and signaling. These data illustrate the dynamic changes in gene expression in the inoculated peach shoots at the transcriptome level. Overall, gene expression in defense response and glycometabolism might result in the gummosis of peach trees induced by L. theobromae.

Transcriptome analysis of highly purified mouse spermatogenic cell populations: gene expression signatures switch from meiotic-to postmeiotic-related processes at pachytene stage

Background

Spermatogenesis is a complex differentiation process that involves the successive and simultaneous execution of three different gene expression programs: mitotic proliferation of spermatogonia, meiosis, and spermiogenesis. Testicular cell heterogeneity has hindered its molecular analyses. Moreover, the characterization of short, poorly represented cell stages such as initial meiotic prophase ones (leptotene and zygotene) has remained elusive, despite their crucial importance for understanding the fundamentals of meiosis.

Results

We have developed a flow cytometry-based approach for obtaining highly pure stage-specific spermatogenic cell populations, including early meiotic prophase. Here we combined this methodology with next generation sequencing, which enabled the analysis of meiotic and postmeiotic gene expression signatures in mouse with unprecedented reliability. Interestingly, we found that a considerable number of genes involved in early as well as late meiotic processes are already on at early meiotic prophase, with a high proportion of them being expressed only for the short time lapse of lepto-zygotene stages. Besides, we observed a massive change in gene expression patterns during medium meiotic prophase (pachytene) when mostly genes related to spermiogenesis and sperm function are already turned on. This indicates that the transcriptional switch from meiosis to post-meiosis takes place very early, during meiotic prophase, thus disclosing a higher incidence of post-transcriptional regulation in spermatogenesis than previously reported. Moreover, we found that a good proportion of the differential gene expression in spermiogenesis corresponds to up-regulation of genes whose expression starts earlier, at pachytene stage; this includes transition protein-and protamine-coding genes, which have long been claimed to switch on during spermiogenesis. In addition, our results afford new insights concerning X chromosome meiotic inactivation and reactivation.

Conclusions

This work provides for the first time an overview of the time course for the massive onset and turning off of the meiotic and spermiogenic genetic programs. Importantly, our data represent a highly reliable information set about gene expression in pure testicular cell populations including early meiotic prophase, for further data mining towards the elucidation of the molecular bases of male reproduction in mammals.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2618-1) contains supplementary material, which is available to authorized users.

Transcriptomic Profile of Whole Blood Cells from Elderly Subjects Fed Probiotic Bacteria Lactobacillus rhamnosus GG ATCC 53103 (LGG) in a Phase I Open Label Study

We examined gene expression of whole blood cells (WBC) from 11 healthy elderly volunteers participating on a Phase I open label study before and after oral treatment with Lactobacillus rhamnosus GG-ATCC 53103 (LGG)) using RNA-sequencing (RNA-Seq). Elderly patients (65–80 yrs) completed a clinical assessment for health status and had blood drawn for cellular RNA extraction at study admission (Baseline), after 28 days of daily LGG treatment (Day 28) and at the end of the study (Day 56) after LGG treatment had been suspended for 28 days. Treatment compliance was verified by measuring LGG-DNA copy levels detected in host fecal samples. Normalized gene expression levels in WBC RNA were analyzed using a paired design built within three analysis platforms (edgeR, DESeq2 and TSPM) commonly used for gene count data analysis. From the 25,990 transcripts detected, 95 differentially expressed genes (DEGs) were detected in common by all analysis platforms with a nominal significant difference in gene expression at Day 28 following LGG treatment (FDR<0.1; 77 decreased and 18 increased). With a more stringent significance threshold (FDR<0.05), only two genes (FCER2 and LY86), were down-regulated more than 1.5 fold and met the criteria for differential expression across two analysis platforms. The remaining 93 genes were only detected at this threshold level with DESeq2 platform. Data analysis for biological interpretation of DEGs with an absolute fold change of 1.5 revealed down-regulation of overlapping genes involved with Cellular movement, Cell to cell signaling interactions, Immune cell trafficking and Inflammatory response. These data provide evidence for LGG-induced transcriptional modulation in healthy elderly volunteers because pre-treatment transcription levels were restored at 28 days after LGG treatment was stopped. To gain insight into the signaling pathways affected in response to LGG treatment, DEG were mapped using biological pathways and genomic data mining packages to indicate significant biological relevance.

Trial Registration: ClinicalTrials.gov NCT01274598

Comparing de novo and reference-based transcriptome assembly strategies by applying them to the blood-sucking bug Rhodnius prolixus

High Throughput Sequencing capabilities have made the process of assembling a transcriptome easier, whether or not there is a reference genome. But the quality of a transcriptome assembly must be good enough to capture the most comprehensive catalog of transcripts and their variations, and to carry out further experiments on transcriptomics. There is currently no consensus on which of the many sequencing technologies and assembly tools are the most effective. Many non-model organisms lack a reference genome to guide the transcriptome assembly. One question, therefore, is whether or not a reference-based genome assembly gives better results than de novo assembly. The blood-sucking insect Rhodnius prolixus-a vector for Chagas disease-has a reference genome. It is therefore a good model on which to compare reference-based and de novo transcriptome assemblies. In this study, we compared de novo and reference-based genome assembly strategies using three datasets (454, Illumina, 454 combined with Illumina) and various assembly software. We developed criteria to compare the resulting assemblies: the size distribution and number of transcripts, the proportion of potentially chimeric transcripts, how complete the assembly was (completeness evaluated both through CEGMA software and R. prolixus proteome fraction retrieved). Moreover, we looked for the presence of two chemosensory gene families (Odorant-Binding Proteins and Chemosensory Proteins) to validate the assembly quality. The reference-based assemblies after genome annotation were clearly better than those generated using de novo strategies alone. Reference-based strategies revealed new transcripts, including new isoforms unpredicted by automatic genome annotation. However, a combination of both de novo and reference-based strategies gave the best result, and allowed us to assemble fragmented transcripts.

Genome-wide transcriptome analysis of hypothalamus in rats with inherited stress-induced arterial hypertension

Background

The hypothalamus has an important role in the onset and maintenance of hypertension and stress responses. Rats with inherited stress-induced arterial hypertension (ISIAH), reproducing the human stress-sensitive hypertensive state with predominant involvement of the neuroendocrine hypothalamic-pituitary-adrenal and sympathoadrenal axes, were used for analysis of the hypothalamus transcriptome.

Results

RNA-seq analysis revealed 139 genes differentially expressed in the hypothalami of hypertensive ISIAH and normotensive Wistar Albino Glaxo (WAG) rats. According to the annotation in databases, 18 of the differentially expressed genes (DEGs) were associated with arterial hypertension. The Gene Ontology (GO) functional annotation showed that these genes were related to different biological processes that may contribute to the hypertension development in the ISIAH rats. The most significantly affected processes were the following: regulation of hormone levels, immune system process, regulation of response to stimulus, blood circulation, response to stress, response to hormone stimulus, transport, metabolic processes, and endocrine system development. The most significantly affected metabolic pathways were those associated with the function of the immune system and cell adhesion molecules and the metabolism of retinol and arachidonic acid. Of the top 40 DEGs making the greatest contribution to the interstrain differences, there were 3 genes (Ephx2, Cst3 and Ltbp2) associated with hypertension that were considered to be suitable for further studies as potential targets for the stress-sensitive hypertension therapy. Seven DEGs were found to be common between hypothalamic transcriptomes of ISIAH rats and Schlager mice with established neurogenic hypertension.

Conclusions

The results of this study revealed multiple DEGs and possible mechanisms specifying the hypothalamic function in the hypertensive ISIAH rats. These results provide a basis for further investigation of the signalling mechanisms that affect hypothalamic output related to stress-sensitive hypertension development.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0307-8) contains supplementary material, which is available to authorized users.

Comparative transcriptional profiling of renal cortex in rats with inherited stress-induced arterial hypertension and normotensive Wistar Albino Glaxo rats

Background

The renal function plays a leading role in long-term control of arterial pressure. The comparative analysis of renal cortex transcriptome in ISIAH rats with inherited stress-induced arterial hypertension and normotensive WAG rats was performed using RNA-Seq approach. The goal of the study was to identify the differentially expressed genes (DEGs) related to hypertension and to detect the pathways contributing to the differences in renal functions in ISIAH and WAG rats.

Results

The analysis revealed 716 genes differentially expressed in renal cortex of ISIAH and WAG rats, 42 of them were associated with arterial hypertension and regulation of blood pressure (BP). Several Gene Ontology (GO) terms significantly enriched with DEGs suggested the existence of the hormone dependent interstrain differences in renal cortex function. Multiple DEGs were associated with regulation of blood pressure and blood circulation, with the response to stress (including oxidative stress, hypoxia, and fluid shear stress) and its regulation. Several other processes which may contribute to hypertension development in ISIAH rats were: ion transport, regulation of calcium ion transport, homeostatic process, tissue remodeling, immune system process and regulation of immune response.

KEGG analysis marked out several pathways significantly enriched with DEGs related to immune system function, to steroid hormone biosynthesis, tryptophan, glutathione, nitrogen, and drug metabolism.

Conclusions

The results of the study provide a basis for identification of potential biomarkers of stress-sensitive hypertension and for further investigation of the mechanisms that affect renal cortex function and hypertension development.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0306-9) contains supplementary material, which is available to authorized users.

Mapping RNA interactions to proteins in virions using CLIP-Seq

RNA nanotechnology often involves protein-RNA complexes that require significant understanding of how the proteins and RNAs contact each other. The CLIP-Seq (cross-linking immunoprecipitation, and DNA sequencing) protocol can be used to probe the RNA molecules that interact with proteins. We have optimized the procedures for RNA fragmentation, immunoprecipitation, and library construction in CLIP-Seq to map the interactions between the RNA and the capsid of a simple positive-strand RNA virus. The results show that distinct portions of the viral RNA contact the capsid. The protocol should be applicable to other RNA virions and also RNA-protein nanoparticles.

Identification of Small Novel Coding Sequences, a Proteogenomics Endeavor

The identification of small proteins and peptides has consistently proven to be challenging. However, technological advances as well as multi-omics endeavors facilitate the identification of novel small coding sequences, leading to new insights. Specifically, the application of next generation sequencing technologies (NGS), providing accurate and sample specific transcriptome / translatome information, into the proteomics field led to more comprehensive results and new discoveries. This book chapter focuses on the inclusion of RNA-Seq and RIBO-Seq also known as ribosome profiling, an RNA-Seq based technique sequencing the +/- 30 bp long fragments captured by translating ribosomes. We emphasize the identification of micropeptides and neo-antigens, two distinct classes of small translation products, triggering our current understanding of biology. RNA-Seq is capable of capturing sample specific genomic variations, enabling focused neo-antigen identification. RIBO-Seq can identify translation events in small open reading frames which are considered to be non-coding, leading to the discovery of micropeptides. The identification of small translation products requires the integration of multi-omics data, stressing the importance of proteogenomics in this novel research area.

Methods to Study Long Noncoding RNA Biology in Cancer

Thousands of long noncoding RNAs (lncRNAs) have been discovered in recent years. The functions of lncRNAs range broadly from regulating chromatin structure and gene expression in the nucleus to controlling messenger RNA (mRNA) processing, mRNA posttranscriptional regulation, cellular signaling, and protein activity in the cytoplasm. Experimental and computational techniques have been developed to characterize lncRNAs in high-throughput scale, to study the lncRNA function in vitro and in vivo, to map lncRNA binding sites on the genome, and to capture lncRNA-protein interactions with the identification of lncRNA-binding partners, binding sites, and interaction determinants. In this chapter, we will discuss these technologies and their applications in decoding the functions of lncRNAs. Understanding these techniques including their advantages and disadvantages and developing them in the future will be essential to elaborate the roles of lncRNAs in cancer and other diseases.

Comprehensive Analysis of the Triterpenoid Saponins Biosynthetic Pathway in Anemone flaccida by Transcriptome and Proteome Profiling

BACKGROUND

Anemone flaccida Fr. Shmidt (Ranunculaceae), commonly known as 'Di Wu' in China, is a perennial herb with limited distribution. The rhizome of A. flaccida has long been used to treat arthritis as a tradition in China. Studies disclosed that the plant contains a rich source of triterpenoid saponins. However, little is known about triterpenoid saponins biosynthesis in A. flaccida.

RESULTS

In this study, we conducted the tandem transcriptome and proteome profiling of a non-model medicinal plant, A. flaccida. Using Illumina HiSeq 2000 sequencing and iTRAQ technique, a total of 46,962 high-quality unigenes were obtained with an average sequence length of 1,310 bp, along with 1473 unique proteins from A. flaccida. Among the A. flaccida transcripts, 36,617 (77.97%) showed significant similarity (E-value < 1e (-5)) to the known proteins in the public database. Of the total 46,962 unigenes, 36,617 open reading frame (ORFs) were predicted. By the fragments per kilobases per million reads (FPKM) statistics, 14,004 isoforms/unigenes were found to be upregulated, and 14,090 isoforms/unigenes were down-regulated in the rhizomes as compared to those in the leaves. Based on the bioinformatics analysis, all possible enzymes involved in the triterpenoid saponins biosynthetic pathway of A. flaccida were identified, including cytosolic mevalonate pathway (MVA) and the plastidial methylerythritol pathway (MEP). Additionally, a total of 126 putative cytochrome P450 (CYP450) and 32 putative UDP glycosyltransferases were selected as the candidates of triterpenoid saponins modifiers. Among them, four of them were annotated as the gene of CYP716A subfamily, the key enzyme in the oleanane-type triterpenoid saponins biosynthetic pathway. Furthermore, based on RNA-Seq and proteome analysis, as well as quantitative RT-PCR verification, the expression level of gene and protein committed to triterpenoids biosynthesis in the leaf versus the rhizome was compared.

CONCLUSION

A combination of the de novo transcriptome and proteome profiling based on the Illumina HiSeq 2000 sequencing platform and iTRAQ technique was shown to be a powerful method for the discovery of candidate genes, which encoded enzymes that were responsible for the biosynthesis of novel secondary metabolites in a non-model plant. The transcriptome data of our study provides a very important resource for the understanding of the triterpenoid saponins biosynthesis of A. flaccida.

Next-Generation Transcriptome Profiling of the Salmon Louse Caligus rogercresseyi Exposed to Deltamethrin (AlphaMax™): Discovery of Relevant Genes and Sex-Related Differences

Sea lice are one of the main parasites affecting the salmon aquaculture industry, causing significant economic losses worldwide. Increased resistance to traditional chemical treatments has created the need to find alternative control methods. Therefore, the objective of this study was to identify the transcriptome response of the salmon louse Caligus rogercresseyi to the delousing drug deltamethrin (AlphaMax™). Through bioassays with different concentrations of deltamethrin, adult salmon lice transcriptomes were sequenced from cDNA libraries in the MiSeq Illumina platform. A total of 78 million reads for females and males were assembled in 30,212 and 38,536 contigs, respectively. De novo assembly yielded 86,878 high-quality contigs and, based on published data, it was possible to annotate and identify relevant genes involved in several biological processes. RNA-seq analysis in conjunction with heatmap hierarchical clustering evidenced that pyrethroids modify the ectoparasitic transcriptome in adults, affecting molecular processes associated with the nervous system, cuticle formation, oxidative stress, reproduction, and metabolism, among others. Furthermore, sex-related transcriptome differences were evidenced. Specifically, 534 and 1033 exclusive transcripts were identified for males and females, respectively, and 154 were shared between sexes. For males, estradiol 17-beta-dehydrogenase, sphingolipid delta4-desaturase DES1, ketosamine-3-kinase, and arylsulfatase A, among others, were discovered, while for females, vitellogenin 1, glycoprotein G, transaldolase, and nitric oxide synthase were among those identified. The shared transcripts included annotations for tropomyosin, γ-crystallin A, glutamate receptor-metabotropic, glutathione S-transferase, and carboxipeptidase B. The present study reveals that deltamethrin generates a complex transcriptome response in C. rogercresseyi, thus providing valuable genomic information for developing new delousing drugs.

Assessing the utility of the Oxford Nanopore MinION for snake venom gland cDNA sequencing

Portable DNA sequencers such as the Oxford Nanopore MinION device have the potential to be truly disruptive technologies, facilitating new approaches and analyses and, in some cases, taking sequencing out of the lab and into the field. However, the capabilities of these technologies are still being revealed. Here we show that single-molecule cDNA sequencing using the MinION accurately characterises venom toxin-encoding genes in the painted saw-scaled viper, Echis coloratus. We find the raw sequencing error rate to be around 12%, improved to 0–2% with hybrid error correction and 3% with de novo error correction. Our corrected data provides full coding sequences and 5′ and 3′ UTRs for 29 of 33 candidate venom toxins detected, far superior to Illumina data (13/40 complete) and Sanger-based ESTs (15/29). We suggest that, should the current pace of improvement continue, the MinION will become the default approach for cDNA sequencing in a variety of species.

Semantic Assembly and Annotation of Draft RNAseq Transcripts without a Reference Genome

Transcriptomes are one of the first sources of high-throughput genomic data that have benefitted from the introduction of Next-Gen Sequencing. As sequencing technology becomes more accessible, transcriptome sequencing is applicable to multiple organisms for which genome sequences are unavailable. Currently all methods for de novo assembly are based on the concept of matching the nucleotide context overlapping between short fragments-reads. However, even short reads may still contain biologically relevant information which can be used as hints in guiding the assembly process. We propose a computational workflow for the reconstruction and functional annotation of expressed gene transcripts that does not require a reference genome sequence and can be tolerant to low coverage, high error rates and other issues that often lead to poor results of de novo assembly in studies of non-model organisms. We start with either raw sequences or the output of a context-based de novo transcriptome assembly. Instead of mapping reads to a reference genome or creating a completely unsupervised clustering of reads, we assemble the unknown transcriptome using nearest homologs from a public database as seeds. We consider even distant relations, indirectly linking protein-coding fragments to entire gene families in multiple distantly related genomes. The intended application of the proposed method is an additional step of semantic (based on relations between protein-coding fragments) scaffolding following traditional (i.e. based on sequence overlap) de novo assembly. The method we developed was effective in analysis of the jellyfish Cyanea capillata transcriptome and may be applicable in other studies of gene expression in species lacking a high quality reference genome sequence. Our algorithms are implemented in C and designed for parallel computation using a high-performance computer. The software is available free of charge via an open source license.

Phenotypic and Transcriptomic Analyses of Autotetraploid and Diploid Mulberry (Morus alba L.)

Autopolyploid plants and their organs are often larger than their diploid counterparts, which makes them attractive to plant breeders. Mulberry (Morus alba L.) is an important commercial woody plant in many tropical and subtropical areas. In this study, we obtained a series of autotetraploid mulberry plants resulting from a colchicine treatment. To evaluate the effects of genome duplications in mulberry, we compared the phenotypes and transcriptomes of autotetraploid and diploid mulberry trees. In the autotetraploids, the height, breast-height diameter, leaf size, and fruit size were larger than those of diploids. Transcriptome data revealed that of 21,229 expressed genes only 609 (2.87%) were differentially expressed between diploids and autotetraploids. Among them, 30 genes were associated with the biosynthesis and signal transduction of plant hormones, including cytokinin, gibberellins, ethylene, and auxin. In addition, 41 differentially expressed genes were involved in photosynthesis. These results enhance our understanding of the variations that occur in mulberry autotetraploids and will benefit future breeding work.

Estimation of isoform expression in RNA-seq data using a hierarchical Bayesian model

Estimation of gene or isoform expression is a fundamental step in many transcriptome analysis tasks, such as differential expression analysis, eQTL (or sQTL) studies, and biological network construction. RNA-seq technology enables us to monitor the expression on genome-wide scale at single base pair resolution and offers the possibility of accurately measuring expression at the level of isoform. However, challenges remain because of non-uniform read sampling and the presence of various biases in RNA-seq data. In this paper, we present a novel hierarchical Bayesian method to estimate isoform expression. While most of the existing methods treat gene expression as a by-product, we incorporate it into our model and explicitly describe its relationship with corresponding isoform expression using a Multinomial distribution. In this way, gene and isoform expression are included in a unified framework and it helps us achieve a better performance over other state-of-the-art algorithms for isoform expression estimation. The effectiveness of the proposed method is demonstrated using both simulated data with known ground truth and two real RNA-seq datasets from MAQC project. The codes are available at http://www.math.pku.edu.cn/teachers/dengmh/GIExp/.

Whole transcriptomic analysis of the plant-beneficial rhizobacterium Bacillus amyloliquefaciens SQR9 during enhanced biofilm formation regulated by maize root exudates

BACKGROUND

Bacillus amyloliquefaciens SQR9 is a plant growth-promoting rhizobacteria (PGPR) with outstanding abilities to enhance plant growth and to control soil-borne diseases. Root exudates is known to play important roles in plant-microbe interactions. To explore the rhizosphere interactions and plant-beneficial characteristics of SQR9, the complete genome sequence as well as the transcriptome in response to maize root exudates under biofilm-forming conditions were elucidated.

RESULTS

Maize root exudates stimulated SQR9 biofilm formation in liquid culture, which is known to be positively correlated with enhanced root colonization. Transcriptional profiling via RNA-sequencing of SQR9 under static conditions indicated that, at 24 h post-inoculation, root exudates stimulated the expression of metabolism-relevant genes, while at 48 h post-inoculation, genes related to extracellular matrix production (tapA-sipW-tasA operon) were activated by root exudates. The individual components in maize root exudates that stimulated biofilm formation included glucose, citric acid, and fumaric acid, which either promoted the growth of SQR9 cells or activated extracellular matrix production. In addition, numerous groups of genes involved in rhizosphere adaptation and in plant-beneficial traits, including plant polysaccharide utilization, cell motility and chemotaxis, secondary antibiotics synthesis clusters, and plant growth promotion-relevant, were identified in the SQR9 genome. These genes also appeared to be induced by the maize root exudates.

CONCLUSIONS

Enhanced biofilm formation of B. amyloliquefaciens SQR9 by maize root exudates could mainly be attributed to promoting cell growth and to inducing extracellular matrix production. The genomic analysis also highlighted the elements involved in the strain's potential as a PGPR. This study provides useful information for understanding plant-rhizobacteria interactions and hence for promoting the agricultural applications of this strain.

MicroRNAs in the pathobiology of sarcomas

Sarcomas are a rare and heterogeneous group of tumors. The last decade has witnessed extensive efforts to understand the pathobiology of many aggressive sarcoma types. In parallel, we have also begun to unravel the complex gene regulation processes mediated by microRNAs (miRNAs) in sarcomas and other cancers, discovering that microRNAs have critical roles in the majority of both oncogenic and tumor suppressor signaling networks. Expression profiles and a greater understanding of the biologic roles of microRNAs and other noncoding RNAs have considerably expanded our current knowledge and provided key pathobiological insights into many sarcomas, and helped identify novel therapeutic targets. The limited number of sarcoma patients in each sarcoma type and their heterogeneity pose distinct challenges in translating this knowledge into the clinic. It will be critical to prioritize these novel targets and choose those that have a broad applicability. A small group of microRNAs have conserved roles across many types of sarcomas and other cancers. Therapies that target these key microRNA-gene signaling and regulatory networks, in combination with standard of care treatment, may be the pivotal component in significantly improving treatment outcomes in patients with sarcoma or other cancers.

Effects of subsampling on characteristics of RNA-seq data from triple-negative breast cancer patients

Background

Data from RNA-seq experiments provide a wealth of information about the transcriptome of an organism. However, the analysis of such data is very demanding. In this study, we aimed to establish robust analysis procedures that can be used in clinical practice.

Methods

We studied RNA-seq data from triple-negative breast cancer patients. Specifically, we investigated the subsampling of RNA-seq data.

Results

The main results of our investigations are as follows: (1) the subsampling of RNA-seq data gave biologically realistic simulations of sequencing experiments with smaller sequencing depth but not direct scaling of count matrices; (2) the saturation of results required an average sequencing depth larger than 32 million reads and an individual sequencing depth larger than 46 million reads; and (3) for an abrogated feature selection, higher moments of the distribution of all expressed genes had a higher sensitivity for signal detection than the corresponding mean values.

Conclusions

Our results reveal important characteristics of RNA-seq data that must be understood before one can apply such an approach to translational medicine.

A Comprehensive Analysis of the Transcriptomes of Marssonina brunnea and Infected Poplar Leaves to Capture Vital Events in Host-Pathogen Interactions

Background

Understanding host-pathogen interaction mechanisms helps to elucidate the entire infection process and focus on important events, and it is a promising approach for improvement of disease control and selection of treatment strategy. Time-course host-pathogen transcriptome analyses and network inference have been applied to unravel the direct or indirect relationships of gene expression alterations. However, time series analyses can suffer from absent time points due to technical problems such as RNA degradation, which limits the application of algorithms that require strict sequential sampling. Here, we introduce an efficient method using independence test to infer an independent network that is exclusively concerned with the frequency of gene expression changes.

Results

Highly resistant NL895 poplar leaves and weakly resistant NL214 leaves were infected with highly active and weakly active Marssonina brunnea, respectively, and were harvested at different time points. The independent network inference illustrated the top 1,000 vital fungus-poplar relationships, which contained 768 fungal genes and 54 poplar genes. These genes could be classified into three categories: a fungal gene surrounded by many poplar genes; a poplar gene connected to many fungal genes; and other genes (possessing low degrees of connectivity). Notably, the fungal gene M6_08342 (a metalloprotease) was connected to 10 poplar genes, particularly including two disease-resistance genes. These core genes, which are surrounded by other genes, may be of particular importance in complicated infection processes and worthy of further investigation.

Conclusions

We provide a clear framework of the interaction network and identify a number of candidate key effectors in this process, which might assist in functional tests, resistant clone selection, and disease control in the future.

Use of RNA sequencing to evaluate rheumatic disease patients

Studying the factors that control gene expression is of substantial importance for rheumatic diseases with poorly understood etiopathogenesis. In the past, gene expression microarrays have been used to measure transcript abundance on a genome-wide scale in a particular cell, tissue or organ. Microarray analysis has led to gene signatures that differentiate rheumatic diseases, and stages of a disease, as well as response to treatments. Nowadays, however, with the advent of next-generation sequencing methods, massive parallel sequencing of RNA tends to be the technology of choice for gene expression profiling, due to several advantages over microarrays, as well as for the detection of non-coding transcripts and alternative splicing events. In this review, we describe how RNA sequencing enables unbiased interrogation of the abundance and complexity of the transcriptome, and present a typical experimental workflow and bioinformatics tools that are often used for RNA sequencing analysis. We also discuss different uses of this next-generation sequencing technology to evaluate rheumatic disease patients and investigate the pathogenesis of rheumatic diseases such as rheumatoid arthritis, systemic lupus erythematosus, juvenile idiopathic arthritis and Sjögren’s syndrome.

PDEGEM: Modeling non-uniform read distribution in RNA-Seq data

Background

RNA-Seq is a powerful new technology to comprehensively analyze the transcriptome of any given cells. An important task in RNA-Seq data analysis is quantifying the expression levels of all transcripts. Although many methods have been introduced and much progress has been made, a satisfactory solution remains be elusive.

Results

In this article, we borrow the idea from the Positional Dependent Nearest Neighborhood (PDNN) model, originally developed for analyzing microarray data, to model the non-uniformity of read distribution in RNA-seq data. We propose a robust nonlinear regression model named PDEGEM, a Positional Dependent Energy Guided Expression Model to estimate the abundance of transcripts. Using real data, we find that the PDEGEM fits the data better than mseq in all three real datasets we tested. We also find that the expression measure obtained using PDEGEM showed higher correlation with that obtained from alterative assays for quantifying gene and isoform expressions.

Conclusions

Based on these results, we believe that our PDEGEM can improve the accuracy in modeling and estimating the transcript abundance and isoform expression in RNA-Seq data. Additionally, although the stacking energy and positional weight of the PDEGEM are relatively related to sequencing platforms and species, they share some common trends, which indicates that the PDEGEM could partly reflect the mechanism of DNA binding between the template strain and the new synthesized read.

The PDEGEM model can be freely downloaded at: http://www.math.pku.edu.cn/teachers/dengmh/PDEGEM.

The gene expression profile of resistant and susceptible Bombyx mori strains reveals cypovirus-associated variations in host gene transcript levels

High-throughput paired-end RNA sequencing (RNA-Seq) was performed to investigate the gene expression profile of a susceptible Bombyx mori strain, Lan5, and a resistant B. mori strain, Ou17, which were both orally infected with B. mori cypovirus (BmCPV) in the midgut. There were 330 and 218 up-regulated genes, while there were 147 and 260 down-regulated genes in the Lan5 and Ou17 strains, respectively. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment for differentially expressed genes (DEGs) were carried out. Moreover, gene interaction network (STRING) analyses were performed to analyze the relationships among the shared DEGs. Some of these genes were related and formed a large network, in which the genes for B. mori cuticular protein RR-2 motif 123 (BmCPR123) and the gene for B. mori DNA replication licensing factor Mcm2-like (BmMCM2) were key genes among the common up-regulated DEGs, whereas the gene for B. mori heat shock protein 20.1 (Bmhsp20.1) was the central gene among the shared down-regulated DEGs between Lan5 vs Lan5-CPV and Ou17 vs Ou17-CPV. These findings established a comprehensive database of genes that are differentially expressed in response to BmCPV infection between silkworm strains that differed in resistance to BmCPV and implied that these DEGs might be involved in B. mori immune responses against BmCPV infection.

TRUFA: A User-Friendly Web Server for de novo RNA-seq Analysis Using Cluster Computing

Application of next-generation sequencing (NGS) methods for transcriptome analysis (RNA-seq) has become increasingly accessible in recent years and are of great interest to many biological disciplines including, eg, evolutionary biology, ecology, biomedicine, and computational biology. Although virtually any research group can now obtain RNA-seq data, only a few have the bioinformatics knowledge and computation facilities required for transcriptome analysis. Here, we present TRUFA (TRanscriptome User-Friendly Analysis), an open informatics platform offering a web-based interface that generates the outputs commonly used in de novo RNA-seq analysis and comparative transcriptomics. TRUFA provides a comprehensive service that allows performing dynamically raw read cleaning, transcript assembly, annotation, and expression quantification. Due to the computationally intensive nature of such analyses, TRUFA is highly parallelized and benefits from accessing high-performance computing resources. The complete TRUFA pipeline was validated using four previously published transcriptomic data sets. TRUFA’s results for the example datasets showed globally similar results when comparing with the original studies, and performed particularly better when analyzing the green tea dataset. The platform permits analyzing RNA-seq data in a fast, robust, and user-friendly manner. Accounts on TRUFA are provided freely upon request at https://trufa.ifca.es.

Omics-based identification of biomarkers for nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a head and neck cancer that is highly found in distinct geographic areas, such as Southeast Asia. The management of NPC remains burdensome as the prognosis is poor due to the late presentation of the disease and the complex nature of NPC pathogenesis. Therefore, it is necessary to find effective molecular markers for early detection and therapeutic measure of NPC. In this paper, the discovery of molecular biomarker for NPC through the emerging omics technologies including genomics, miRNA-omics, transcriptomics, proteomics, and metabolomics will be extensively reviewed. These markers have been shown to play roles in various cellular pathways in NPC progression. The knowledge on their function will help us understand in more detail the complexity in tumor biology, leading to the better strategies for early detection, outcome prediction, detection of disease recurrence, and therapeutic approach.

Molecular histology of lung cancer: from targets to treatments

Lung cancer is the leading cause of cancer-related death worldwide with a 5-year survival rate of less than 15%, despite significant advances in both diagnostic and therapeutic approaches. Combined genomic and transcriptomic sequencing studies have identified numerous genetic driver mutations that are responsible for the development of lung cancer. In addition, molecular profiling studies identify gene products and their mutations which predict tumour responses to targeted therapies such as protein tyrosine kinase inhibitors and also can offer explanation for drug resistance mechanisms. The profiling of circulating micro-RNAs has also provided an ability to discriminate patients in terms of prognosis/diagnosis and high-throughput DNA sequencing strategies are beginning to elucidate cell signalling pathway mutations associated with oncogenesis, including potential stem cell associated pathways, offering the promise that future therapies may target this sub-population, preventing disease relapse post treatment and improving patient survival. This review provides an assessment of molecular profiling within lung cancer concerning molecular mechanisms, treatment options and disease-progression. Current areas of development within lung cancer profiling are discussed (i.e. profiling of circulating tumour cells) and future challenges for lung cancer treatment addressed such as detection of micro-metastases and cancer stem cells.

SplicingTypesAnno: annotating and quantifying alternative splicing events for RNA-Seq data

Alternative splicing plays a key role in the regulation of the central dogma. Four major types of alternative splicing have been classified as intron retention, exon skipping, alternative 5 splice sites or alternative donor sites, and alternative 3 splice sites or alternative acceptor sites. A few algorithms have been developed to detect splice junctions from RNA-Seq reads. However, there are few tools targeting at the major alternative splicing types at the exon/intron level. This type of analysis may reveal subtle, yet important events of alternative splicing, and thus help gain deeper understanding of the mechanism of alternative splicing. This paper describes a user-friendly R package, extracting, annotating and analyzing alternative splicing types for sequence alignment files from RNA-Seq. SplicingTypesAnno can: (1) provide annotation for major alternative splicing at exon/intron level. By comparing the annotation from GTF/GFF file, it identifies the novel alternative splicing sites; (2) offer a convenient two-level analysis: genome-scale annotation for users with high performance computing environment, and gene-scale annotation for users with personal computers; (3) generate a user-friendly web report and additional BED files for IGV visualization. SplicingTypesAnno is a user-friendly R package for extracting, annotating and analyzing alternative splicing types at exon/intron level for sequence alignment files from RNA-Seq. It is publically available at https://sourceforge.net/projects/splicingtypes/files/ or http://genome.sdau.edu.cn/research/software/SplicingTypesAnno.html.

The effect on the transcriptome of Anemone coronaria following infection with rust (Tranzschelia discolor)

In order to understand plant/pathogen interaction, the transcriptome of uninfected (1S) and infected (2I) plant was sequenced at 3'end by the GS FLX 454 platform. De novo assembly of high-quality reads generated 27,231 contigs leaving 37,191 singletons in the 1S and 38,393 in the 2I libraries. ESTcalc tool suggested that 71% of the transcriptome had been captured, with 99% of the genes present being represented by at least one read. Unigene annotation showed that 50.5% of the predicted translation products shared significant homology with protein sequences in GenBank. In all 253 differential transcript abundance (DTAs) were in higher abundance and 52 in lower abundance in the 2I library. 128 higher abundance DTA genes were of fungal origin and 49 were clearly plant sequences. A tBLASTn-based search of the sequences using as query the full length predicted polypeptide product of 50 R genes identified 16 R gene products. Only one R gene (PGIP) was up-regulated. The response of the plant to fungal invasion included the up-regulation of several pathogenesis related protein (PR) genes involved in JA signaling and other genes associated with defense response and down regulation of cell wall associated genes, non-race-specific disease resistance1 (NDR1) and other genes like myb, presqualene diphosphate phosphatase (PSDPase), a UDP-glycosyltransferase 74E2-like (UGT). The DTA genes identified here should provide a basis for understanding the A. coronaria/T. discolor interaction and leads for biotechnology-based disease resistance breeding.

RNA-Seq: Improving Our Understanding of Retinal Biology and Disease

Over the past several years, rapid technological advances have allowed for a dramatic increase in our knowledge and understanding of the transcriptional landscape, because of the ability to study gene expression in greater depth and with more detail than previously possible. To this end, RNA-Seq has quickly become one of the most widely used methods for studying transcriptomes of tissues and individual cells. Unlike previously favored analysis methods, RNA-Seq is extremely high-throughput, and is not dependent on an annotated transcriptome, laying the foundation for novel genetic discovery. Additionally, RNA-Seq derived transcriptomes provide a basis for widening the scope of research to identify potential targets in the treatment of retinal disease.

Bridger: a new framework for de novo transcriptome assembly using RNA-seq data

We present a new de novo transcriptome assembler, Bridger, which takes advantage of techniques employed in Cufflinks to overcome limitations of the existing de novo assemblers. When tested on dog, human, and mouse RNA-seq data, Bridger assembled more full-length reference transcripts while reporting considerably fewer candidate transcripts, hence greatly reducing false positive transcripts in comparison with the state-of-the-art assemblers. It runs substantially faster and requires much less memory space than most assemblers. More interestingly, Bridger reaches a comparable level of sensitivity and accuracy with Cufflinks. Bridger is available at https://sourceforge.net/projects/rnaseqassembly/files/?source=navbar.

Methods of integrating data to uncover genotype-phenotype interactions

Recent technological advances have expanded the breadth of available omic data, from whole-genome sequencing data, to extensive transcriptomic, methylomic and metabolomic data. A key goal of analyses of these data is the identification of effective models that predict phenotypic traits and outcomes, elucidating important biomarkers and generating important insights into the genetic underpinnings of the heritability of complex traits. There is still a need for powerful and advanced analysis strategies to fully harness the utility of these comprehensive high-throughput data, identifying true associations and reducing the number of false associations. In this Review, we explore the emerging approaches for data integration - including meta-dimensional and multi-staged analyses - which aim to deepen our understanding of the role of genetics and genomics in complex outcomes. With the use and further development of these approaches, an improved understanding of the relationship between genomic variation and human phenotypes may be revealed.

Impact of next-generation technologies on exploring socioeconomically important parasites and developing new interventions

High-throughput molecular and computer technologies have become instrumental for systems biological explorations of pathogens, including parasites. For instance, investigations of the transcriptomes of different developmental stages of parasitic nematodes give insights into gene expression, regulation and function in a parasite, which is a significant step to understanding their biology, as well as interactions with their host(s) and disease. This chapter (1) gives a background on some key parasitic nematodes of socioeconomic importance, (2) describes sequencing and bioinformatic technologies for large-scale studies of the transcriptomes and genomes of these parasites, (3) provides some recent examples of applications and (4) emphasizes the prospects of fundamental biological explorations of parasites using these technologies for the development of new interventions to combat parasitic diseases.

Mango (Mangifera indica L.) cv. Kent fruit mesocarp de novo transcriptome assembly identifies gene families important for ripening

Fruit ripening is a physiological and biochemical process genetically programmed to regulate fruit quality parameters like firmness, flavor, odor and color, as well as production of ethylene in climacteric fruit. In this study, a transcriptomic analysis of mango (Mangifera indica L.) mesocarp cv. "Kent" was done to identify key genes associated with fruit ripening. Using the Illumina sequencing platform, 67,682,269 clean reads were obtained and a transcriptome of 4.8 Gb. A total of 33,142 coding sequences were predicted and after functional annotation, 25,154 protein sequences were assigned with a product according to Swiss-Prot database and 32,560 according to non-redundant database. Differential expression analysis identified 2,306 genes with significant differences in expression between mature-green and ripe mango [1,178 up-regulated and 1,128 down-regulated (FDR ≤ 0.05)]. The expression of 10 genes evaluated by both qRT-PCR and RNA-seq data was highly correlated (R = 0.97), validating the differential expression data from RNA-seq alone. Gene Ontology enrichment analysis, showed significantly represented terms associated to fruit ripening like "cell wall," "carbohydrate catabolic process" and "starch and sucrose metabolic process" among others. Mango genes were assigned to 327 metabolic pathways according to Kyoto Encyclopedia of Genes and Genomes database, among them those involved in fruit ripening such as plant hormone signal transduction, starch and sucrose metabolism, galactose metabolism, terpenoid backbone, and carotenoid biosynthesis. This study provides a mango transcriptome that will be very helpful to identify genes for expression studies in early and late flowering mangos during fruit ripening.

Diminishing returns in next-generation sequencing (NGS) transcriptome data

RNA-seq is increasingly used to study gene expression of various organisms. While it provides a great opportunity to explore genome-scale transcriptional patterns with tremendous depth, it comes with prohibitive costs. Establishing a minimal sequencing depth for required accuracy will guide cost-effective experimental design and promote the routine application of RNA-seq. To address this issue, we selected 36 RNA-seq datasets, each with more than 20 million reads from six widely-used model organisms: Saccharomyces cerevisiae, Homo sapiens, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, and Arabidopsis thaliana, and investigated statistical correlations between the sequencing depth and the outcome accuracy. To achieve this, we randomly chose reads from each dataset, mapped them to the reference genomes, and analyzed the accuracy achieved with varying coverage. Our results indicated that as low as one million reads can provide the same sequencing accuracy in transcript abundance (r=0.99) as >30 million reads for highly-expressed genes in all six species. Because many metabolically and pathologically-relevant genes are highly expressed, our findings might be instructive for cost-effective experimental designs in NGS-based research and also provide useful guidance to similar research for other organisms.

An RNA-seq transcriptome analysis of floral buds of an interspecific Brassica hybrid between B. carinata and B. napus

Interspecific hybridizations promote gene transfer between species and play an important role in plant speciation and crop improvement. However, hybrid sterility that commonly found in the first generation of hybrids hinders the utilization of interspecific hybridization. The combination of divergent parental genomes can create extensive transcriptome variations, and to determine these gene expression alterations and their effects on hybrids, an interspecific Brassica hybrid of B. carinata × B. napus was generated. Scanning electron microscopy analysis indicated that some of the hybrid pollen grains were irregular in shape and exhibited abnormal exine patterns compared with those from the parents. Using the Illumina HiSeq 2000 platform, 39,598, 32,403 and 42,208 genes were identified in flower buds of B. carinata cv. W29, B. napus cv. Zhongshuang 11 and their hybrids, respectively. The differentially expressed genes were significantly enriched in pollen wall assembly, pollen exine formation, pollen development, pollen tube growth, pollination, gene transcription, macromolecule methylation and translation, which might be associated with impaired fertility in the F1 hybrid. These results will shed light on the mechanisms underlying the low fertility of the interspecific hybrids and expand our knowledge of interspecific hybridization.

Natural genetic variation impacts expression levels of coding, non-coding, and antisense transcripts in fission yeast

Our current understanding of how natural genetic variation affects gene expression beyond well-annotated coding genes is still limited. The use of deep sequencing technologies for the study of expression quantitative trait loci (eQTLs) has the potential to close this gap. Here, we generated the first recombinant strain library for fission yeast and conducted an RNA-seq-based QTL study of the coding, non-coding, and antisense transcriptomes. We show that the frequency of distal effects (trans-eQTLs) greatly exceeds the number of local effects (cis-eQTLs) and that non-coding RNAs are as likely to be affected by eQTLs as protein-coding RNAs. We identified a genetic variation of swc5 that modifies the levels of 871 RNAs, with effects on both sense and antisense transcription, and show that this effect most likely goes through a compromised deposition of the histone variant H2A.Z. The strains, methods, and datasets generated here provide a rich resource for future studies.

WemIQ: an accurate and robust isoform quantification method for RNA-seq data

MOTIVATION

The deconvolution of isoform expression from RNA-seq remains challenging because of non-uniform read sampling and subtle differences among isoforms.

RESULTS

We present a weighted-log-likelihood expectation maximization method on isoform quantification (WemIQ). WemIQ integrates an effective bias removal with a weighted expectation maximization (EM) algorithm to distribute reads among isoforms efficiently. The weight represents the oversampling or undersampling of sequence reads and is estimated through a generalized Poisson model without any presumption on the bias sources and formats. WemIQ significantly improves the quantification of isoform and gene expression as well as the derived exon inclusion rates. It provides robust expression estimates across different laboratories and protocols, which is valuable for the integrative analysis of RNA-seq. For the recent single-cell RNA-seq data, WemIQ also provides the opportunity to distinguish bias heterogeneity from true biological heterogeneity and uncovers smaller cell-to-cell expression variability.

Densovirus is a mutualistic symbiont of a global crop pest (Helicoverpa armigera) and protects against a baculovirus and Bt biopesticide

Mutualistic associations between symbiotic bacteria and their hosts are common within insect systems. However, viruses are often considered as pathogens even though some have been reported to be beneficial to their hosts. Herein, we report a novel densovirus, Helicoverpa armigera densovirus-1 (HaDNV-1) that appears to be beneficial to its host. HaDNV-1 was found to be widespread in wild populations of H. armigera adults (>67% prevalence between 2008 and 2012). In wild larval populations, there was a clear negative interaction between HaDNV-1 and H. armigera nucleopolyhedrovirus (HaNPV), a baculovirus that is widely used as a biopesticide. Laboratory bioassays revealed that larvae hosting HaDNV-1 had significantly enhanced resistance to HaNPV (and lower viral loads), and that resistance to Bacillus thuringiensis (Bt) toxin was also higher at low doses. Laboratory assays indicated that the virus was mainly distributed in the fat body, and could be both horizontally- and vertically-transmitted, though the former occurred only at large challenge doses. Densovirus-positive individuals developed more quickly and had higher fecundity than uninfected insects. We found no evidence for a negative effect of HaDNV-1 infection on H. armigera fitness-related traits, strongly suggesting a mutualistic interaction between the cotton bollworm and its densovirus.

The old world cotton bollworm, Helicoverpa armigera, is one of the most significant pests of crops throughout Asia, Europe, Africa and Australia. Herein, we report a novel densovirus (HaDNV-1) which was widely distributed in wild populations of H. armigera and was beneficial to its host by increasing larval and pupal development rates, female lifespan and fecundity, suggesting a mutualistic interaction between the cotton bollworm and HaDNV-1. The cotton bollworm is currently widely controlled by the biopesticides Bacillus thuringiensis (Bt) toxin and the baculovirus HaNPV. It is therefore important to estimate the risk that the symbiotic virus will negatively impact on the efficiency of these biopesticides. Field and laboratory results suggest that HaDNV-1 infection significantly increases larval resistance to HaNPV and Bt toxin. These results have important implications for the selection of biopesticides for this species, and highlight the need for greater research into the elegant microbial interactions that may impact host individual and population dynamics.