Complementary DNA sequencing: expressed sequence tags and human genome project

Transcriptome analysis reveals the molecular mechanisms underlying growth superiority in a novel grouper hybrid (Epinephelus fuscogutatus♀ × E. lanceolatus♂)

BACKGROUND

Groupers (Epinephelus spp.) have been widely cultivated in China and South-East Asian countries. As a novel hybrid offspring crossed between E. fuscogutatus♀ and E. lanceolatus♂, Hulong grouper exhibits significant growth superiority over its female parent, which made it a promising farmed species in grouper aquaculture industry in China. Hulong grouper present a good combination of beneficial traits from both parent species, but the molecular mechanisms of its heterosis still remain poorly understood.

RESULTS

Based on RNA sequencing and gene expression profiling, we conducted comparative transcriptome analyses between Hulong grouper and its parents E. fuscoguttatus & E. lanceolatus. Six hundred sixty-two and 5239 differentially expressed genes (DEGs) were identified in the brains and livers, respectively. GO enrichment analysis of these DEGs revealed that metabolic process and catalytic activity were the most enriched GO terms. Further analysis showed the expressions of GnRH1 and GnRH3 in the brain, and GH/IGF axis related genes such as IGF-1, IGF-2b, IGFBP-1, IGFBP-2, IGFBP-4 and IGFBP-5a in the liver of the hybrid F1 were significantly up-regulated, which is in accordance with the growth superiority of hybrid grouper. Meanwhile, expressions of genes related to the protein and glycogen synthesis pathway, such as PI3KC, PI3KR, Raptor, EIF4E3, and PP1 were up-regulated, while PYG expression was down-regulated. These changes might contribute to increased protein and glycogen synthesis in the hybrid grouper.

CONCLUSIONS

We identified a number of differentially expressed genes such as GnRH1 and GnRH3, and genes involved in GH/IGF axis and its downstream signaling pathways for protein and glycogen synthesis in Hulong Grouper. These findings provided molecular basis underlying growth superiority of hybrid grouper, and comprehensive insights into better understanding the molecular mechanisms and regulative pathways regulating heterosis in fish.

Genome-wide identification and characterization of the apple (Malus domestica) HECT ubiquitin-protein ligase family and expression analysis of their responsiveness to abiotic stresses

The ubiquitin-protein ligases (E3s) directly participate in ubiquitin (Ub) transferring to the target proteins in the ubiquitination pathway. The HECT ubiquitin-protein ligase (UPL), one type of E3s, is characterized as containing a conserved HECT domain of approximately 350 amino acids in the C terminus. Some UPLs were found to be involved in trichome development and leaf senescence in Arabidopsis. However, studies on plant UPLs, such as characteristics of the protein structure, predicted functional motifs of the HECT domain, and the regulatory expression of UPLs have all been limited. Here, we present genome-wide identification of the genes encoding UPLs (HECT gene) in apple. The 13 genes (named as MdUPL1-MdUPL13) from ten different chromosomes were divided into four groups by phylogenetic analysis. Among these groups, the encoding genes in the intron-exon structure and the included additional functional domains were quite different. Notably, the F-box domain was first found in MdUPL7 in plant UPLs. The HECT domain in different MdUPL groups also presented different spatial features and three types of conservative motifs were identified. The promoters of each MdUPL member carried multiple stress-response related elements by cis-acting element analysis. Experimental results demonstrated that the expressions of several MdUPLs were quite sensitive to cold-, drought-, and salt-stresses by qRT-PCR assay. The results of this study helped to elucidate the functions of HECT proteins, especially in Rosaceae plants.

The History of Patenting Genetic Material

The US Supreme Court's recent decision in Association for Molecular Pathology v. Myriad Genetics, Inc. declared, for the first time, that isolated human genes cannot be patented. Many have wondered how genes were ever the subjects of patents. The answer lies in a nuanced understanding of both legal and scientific history. Since the early twentieth century, "products of nature" were not eligible to be patented unless they were "isolated and purified" from their surrounding environment. As molecular biology advanced, and the capability to isolate genes both physically and by sequence came to fruition, researchers (and patent offices) began to apply patent-law logic to genes themselves. These patents, along with other biological patents, generated substantial social and political criticism. Myriad Genetics, a company with patents on BRCA1 and BRCA2, two genes critical to assessing early-onset breast and ovarian cancer risk, and with a particularly controversial business approach, became the antagonist in an ultimately successful campaign to overturn gene patents in court. Despite Myriad's defeat, some questions concerning the rights to monopolize genetic information remain. The history leading to that defeat may be relevant to these future issues.

First-Principles Investigation of Nanopore Sequencing Using Variable Voltage Bias on Graphene-Based Nanoribbons

In this study, we examine the mechanism of nanopore-based DNA sequencing using a voltage bias across a graphene nanoribbon. Using density function theory and a nonequilibrium Green's function approach, we determine the transmission spectra and current profile for adenine, guanine, cytosine, thymine, and uracil as a function of bias voltage in an energy minimized configuration. Utilizing the transmission current, we provide a general methodology for the development of a three nanopore graphene-based device that can be used to distinguish between the various nucleobases for DNA/RNA sequencing. From our analysis, we deduce that it is possible to use different transverse currents across a multinanopore device to differentiate between nucleobases using various voltages of 0.5, 1.3, and 1.6 V. Overall, our goal is to improve nanopore design to further DNA/RNA nucleobase sequencing and biomolecule identification techniques.

Comprehensive comparison of large-scale tissue expression datasets

For tissues to carry out their functions, they rely on the right proteins to be present. Several high-throughput technologies have been used to map out which proteins are expressed in which tissues; however, the data have not previously been systematically compared and integrated. We present a comprehensive evaluation of tissue expression data from a variety of experimental techniques and show that these agree surprisingly well with each other and with results from literature curation and text mining. We further found that most datasets support the assumed but not demonstrated distinction between tissue-specific and ubiquitous expression. By developing comparable confidence scores for all types of evidence, we show that it is possible to improve both quality and coverage by combining the datasets. To facilitate use and visualization of our work, we have developed the TISSUES resource (http://tissues.jensenlab.org), which makes all the scored and integrated data available through a single user-friendly web interface.

Identification and Characterization of a Novel Microvitellogenin from the Chinese Oak Silkworm Antheraea pernyi

Microvitellogenin (mVg) is a relatively small vitellogenic protein only characterized in the eggs of the lepidopteran insects Manduca sexta and Bombyx mori. In the present study, we report a novel mVg (ApmVg) isolated from the Chinese oak silkworm Antheraea pernyi. The obtained ApmVg cDNA sequence contains an open reading frame of 783 bp encoding a protein of 260 amino acids with a predicted molecular weight of 29.96 kDa. This gene does not contain introns. Structural analysis revealed that this protein shares putative conserved domains with the lepidopteran low-molecular weight lipoprotein, which belongs to the lipoprotein_11 superfamily. The protein sequence of ApmVg exhibits 48% sequence identity with mVg from M. sexta and 40-47% sequence identity with the 30K lipoproteins from B. mori. Phylogenetic analysis suggests that ApmVg is a novel member of the lepidopteran low-molecular weight lipoproteins. Transcriptional analysis indicated that ApmVg mRNA is mainly expressed in the fat body (both female and male) during post-diapause development of the pupal stage, and it was also detected in ovaries and spermaries in smaller amounts. RT-PCR and Western blot analyses revealed that ApmVg is synthesized by the fat body and secreted into hemolymph and ultimately accumulates in eggs. The ApmVg transcript can be detected in the fat bodies of female pupae four days after treatment with 20-hydroxyecdysone and shows an expression pattern distinct from that of vitellogenin (Vg), which is detectable throughout diapausing and in post-diapause development. ApmVg decreased dramatically during embryonic development. These results represent the first study of mVg outside M. sexta and B. mori and provide insight into the physiological role and evolution of mVgs.

How Many Genes Are Expressed in a Transcriptome? Estimation and Results for RNA-Seq

RNA-seq experiments estimate the number of genes expressed in a transcriptome as well as their relative frequencies. However, an undetermined number of genes can remain undetected due to their low expression relative to the sample size (sequence depth). Estimation of the true number of genes expressed in a transcriptome is essential in order to determine which genes are exclusively expressed in specific tissues or under particular conditions. A reliable estimate of the true number of expressed genes is also required to accurately measure transcriptome changes and to predict the sequencing depth needed to increase the proportion of detected genes. This problem is analogous to ecological sampling problems such as estimating the number of species at a given site. Here we present a non-parametric estimator for the number of undetected genes as well as for the extra sample size needed to detect a given proportion of the undetected genes. Our estimators are superior to ones already published by having smaller standard errors and biases. We applied our method to a set of 32 publicly available RNA-seq experiments, including the evaluation of 311 individually sequenced libraries. We found that in the majority of the cases more than one thousand genes are undetected, and that on average approximately 6% of the expressed genes per accession remain undetected. This figure increases to approximately 10% if individual sequencing libraries are analyzed. Our method is also applicable to metagenomic experiments. Using our method, the number of undetected genes as well as the sample size needed to detect them can be calculated, leading to more accurate and complete gene expression studies.

Whole transcriptome analysis with sequencing: methods, challenges and potential solutions

Whole transcriptome analysis plays an essential role in deciphering genome structure and function, identifying genetic networks underlying cellular, physiological, biochemical and biological systems and establishing molecular biomarkers that respond to diseases, pathogens and environmental challenges. Here, we review transcriptome analysis methods and technologies that have been used to conduct whole transcriptome shotgun sequencing or whole transcriptome tag/target sequencing analyses. We focus on how adaptors/linkers are added to both 5' and 3' ends of mRNA molecules for cloning or PCR amplification before sequencing. Challenges and potential solutions are also discussed. In brief, next generation sequencing platforms have accelerated releases of the large amounts of gene expression data. It is now time for the genome research community to assemble whole transcriptomes of all species and collect signature targets for each gene/transcript, and thus use known genes/transcripts to determine known transcriptomes directly in the near future.

Dissection of the style's response to pollination using transcriptome profiling in self-compatible (Solanum pimpinellifolium) and self-incompatible (Solanum chilense) tomato species

BACKGROUND

Tomato (Solanum lycopersicum) self-compatibility (SC) is defined as self-pollen tubes that can penetrate their own stigma, elongate in the style and fertilize their own ovules. Self-incompatibility (SI) is defined as self-pollen tubes that are prevented from developing in the style. To determine the influence of gene expression on style self-pollination, a transcriptome-wide comparative analysis of SC and SI tomato unpollinated/pollinated styles was performed using RNA-sequencing (RNA-seq) data.

RESULTS

Transcriptome profiles of 24-h unpollination (UP) and self-pollination (P) styles from SC and SI tomato species were generated using high-throughput next generation sequencing. From the comparison of SC self-pollinated and unpollinated styles, 1341 differentially expressed genes (DEGs) were identified, of which 753 were downregulated and 588 were upregulated. From the comparison of SI self-pollinated and unpollinated styles, 804 DEGs were identified, of which 215 were downregulated and 589 were upregulated. Nine gene ontology (GO) terms were enriched significantly in SC and 78 GO terms were enriched significantly in SI. A total of 105 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified in SC and 80 enriched KEGG pathways were identified in SI, among which "Cysteine and methionine metabolism pathway" and "Plant hormone signal transduction pathway" were significantly enriched in SI.

CONCLUSIONS

This study is the first global transcriptome-wide comparative analysis of SC and SI tomato unpollinated/pollinated styles. Advanced bioinformatic analysis of DEGs uncovered the pathways of "Cysteine and methionine metabolism" and "Plant hormone signal transduction", which are likely to play important roles in the control of pollen tubes growth in SI species.

RNA Sequencing and Analysis

RNA sequencing (RNA-Seq) uses the capabilities of high-throughput sequencing methods to provide insight into the transcriptome of a cell. Compared to previous Sanger sequencing- and microarray-based methods, RNA-Seq provides far higher coverage and greater resolution of the dynamic nature of the transcriptome. Beyond quantifying gene expression, the data generated by RNA-Seq facilitate the discovery of novel transcripts, identification of alternatively spliced genes, and detection of allele-specific expression. Recent advances in the RNA-Seq workflow, from sample preparation to library construction to data analysis, have enabled researchers to further elucidate the functional complexity of the transcription. In addition to polyadenylated messenger RNA (mRNA) transcripts, RNA-Seq can be applied to investigate different populations of RNA, including total RNA, pre-mRNA, and noncoding RNA, such as microRNA and long ncRNA. This article provides an introduction to RNA-Seq methods, including applications, experimental design, and technical challenges.

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.

Experimental strategies for the identification and characterization of adhesive proteins in animals: a review

Adhesive secretions occur in both aquatic and terrestrial animals, in which they perform diverse functions. Biological adhesives can therefore be remarkably complex and involve a large range of components with different functions and interactions. However, being mainly protein based, biological adhesives can be characterized by classical molecular methods. This review compiles experimental strategies that were successfully used to identify, characterize and obtain the full-length sequence of adhesive proteins from nine biological models: echinoderms, barnacles, tubeworms, mussels, sticklebacks, slugs, velvet worms, spiders and ticks. A brief description and practical examples are given for a variety of tools used to study adhesive molecules at different levels from genes to secreted proteins. In most studies, proteins, extracted from secreted materials or from adhesive organs, are analysed for the presence of post-translational modifications and submitted to peptide sequencing. The peptide sequences are then used directly for a BLAST search in genomic or transcriptomic databases, or to design degenerate primers to perform RT-PCR, both allowing the recovery of the sequence of the cDNA coding for the investigated protein. These sequences can then be used for functional validation and recombinant production. In recent years, the dual proteomic and transcriptomic approach has emerged as the best way leading to the identification of novel adhesive proteins and retrieval of their complete sequences.

RNA-Seq analysis and transcriptome assembly for blackberry (Rubus sp. Var. Lochness) fruit

Background

There is an increasing interest in berries, especially blackberries in the diet, because of recent reports of their health benefits due to their high content of flavonoids. A broad range of genomic tools are available for other Rosaceae species but these tools are still lacking in the Rubus genus, thus limiting gene discovery and the breeding of improved varieties.

Results

De novo RNA-seq of ripe blackberries grown under field conditions was performed using Illumina Hiseq 2000. Almost 9 billion nucleotide bases were sequenced in total. Following assembly, 42,062 consensus sequences were detected. For functional annotation, 33,040 (NR), 32,762 (NT), 21,932 (Swiss-Prot), 20,134 (KEGG), 13,676 (COG), 24,168 (GO) consensus sequences were annotated using different databases; in total 34,552 annotated sequences were identified. For protein prediction analysis, the number of coding DNA sequences (CDS) that mapped to the protein database was 32,540. Non redundant (NR), annotation showed that 25,418 genes (73.5%) has the highest similarity with Fragaria vesca subspecies vesca. Reanalysis was undertaken by aligning the reads with this reference genome for a deeper analysis of the transcriptome. We demonstrated that de novo assembly, using Trinity and later annotation with Blast using different databases, were complementary to alignment to the reference sequence using SOAPaligner/SOAP2. The Fragaria reference genome belongs to a species in the same family as blackberry (Rosaceae) but to a different genus. Since blackberries are tetraploids, the possibility of artefactual gene chimeras resulting from mis-assembly was tested with one of the genes sequenced by RNAseq, Chalcone Synthase (CHS). cDNAs encoding this protein were cloned and sequenced. Primers designed to the assembled sequences accurately distinguished different contigs, at least for chalcone synthase genes.

Conclusions

We prepared and analysed transcriptome data from ripe blackberries, for which prior genomic information was limited. This new sequence information will improve the knowledge of this important and healthy fruit, providing an invaluable new tool for biological research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-014-1198-1) contains supplementary material, which is available to authorized users.

High-throughput plasmid construction using homologous recombination in yeast: its mechanisms and application to protein production for X-ray crystallography

Homologous recombination is a system for repairing the broken genomes of living organisms by connecting two DNA strands at their homologous sequences. Today, homologous recombination in yeast is used for plasmid construction as a substitute for traditional methods using restriction enzymes and ligases. This method has various advantages over the traditional method, including flexibility in the position of DNA insertion and ease of manipulation. Recently, the author of this review reported the construction of plasmids by homologous recombination in the methanol-utilizing yeast Pichia pastoris, which is known to be an excellent expression host for secretory proteins and membrane proteins. The method enabled high-throughput construction of expression systems of proteins using P. pastoris; the constructed expression systems were used to investigate the expression conditions of membrane proteins and to perform X-ray crystallography of secretory proteins. This review discusses the mechanisms and applications of homologous recombination, including the production of proteins for X-ray crystallography.

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.

Mining diverse small RNA species in the deep transcriptome

Transcriptomes of many species are proving to be exquisitely diverse, and many investigators are now using high-throughput sequencing to quantify non-protein-coding RNAs, namely small RNAs (sRNA). Unfortunately, most studies are focused solely on microRNA changes, and many investigators are not analyzing the full compendium of sRNA species present in their large datasets. We provide here a rationale to include all types of sRNAs in sRNA sequencing analyses, which will aid in the discovery of their biological functions and physiological relevance.

trieFinder: an efficient program for annotating Digital Gene Expression (DGE) tags

Background

Quantification of a transcriptional profile is a useful way to evaluate the activity of a cell at a given point in time. Although RNA-Seq has revolutionized transcriptional profiling, the costs of RNA-Seq are still significantly higher than microarrays, and often the depth of data delivered from RNA-Seq is in excess of what is needed for simple transcript quantification. Digital Gene Expression (DGE) is a cost-effective, sequence-based approach for simple transcript quantification: by sequencing one read per molecule of RNA, this technique can be used to efficiently count transcripts while obviating the need for transcript-length normalization and reducing the total numbers of reads necessary for accurate quantification. Here, we present trieFinder, a program specifically designed to rapidly map, parse, and annotate DGE tags of various lengths against cDNA and/or genomic sequence databases.

Results

The trieFinder algorithm maps DGE tags in a two-step process. First, it scans FASTA files of RefSeq, UniGene, and genomic DNA sequences to create a database of all tags that can be derived from a predefined restriction site. Next, it compares the experimental DGE tags to this tag database, taking advantage of the fact that the tags are stored as a prefix tree, or “trie”, which allows for linear-time searches for exact matches. DGE tags with mismatches are analyzed by recursive calls in the data structure. We find that, in terms of alignment speed, the mapping functionality of trieFinder compares favorably with Bowtie.

Conclusions

trieFinder can quickly provide the user an annotation of the DGE tags from three sources simultaneously, simplifying transcript quantification and novel transcript detection, delivering the data in a simple parsed format, obviating the need to post-process the alignment results. trieFinder is available at http://research.nhgri.nih.gov/software/trieFinder/.

Advances in identification and validation of plant microRNAs and their target genes

Developments in the field of molecular biology and genetics, such as microarray, gene transfer and discovery of small regulatory RNAs, have led to significant advances in plant biotechnology. Among the small RNAs, microRNAs (miRNAs) have elicited much interest as key post-transcriptional regulators in eukaryotic gene expression. Advances in genome and transcriptome sequencing of plants have facilitated the generation of a huge wealth of sequence information that can find much use in the discovery of novel miRNAs and their target genes. In this review, we present an overview of the developments in the strategies and methods used to identify and study miRNAs, their target genes and the mechanisms by which these miRNAs interact with their target genes since the discovery of the first miRNA. The approaches discussed include both reverse and forward genetics. We observed that despite the availability of advanced methods, certain limitations ranging from the cost of materials, equipment and personnel to the availability of genome sequences for many plant species present a number of challenges for the development and utilization of modern scientific methods for the elucidation and development of miRNAs in many important plant species.

Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding

Marker-assisted selection (MAS) refers to the use of molecular markers to assist phenotypic selections in crop improvement. Several types of molecular markers, such as single nucleotide polymorphism (SNP), have been identified and effectively used in plant breeding. The application of next-generation sequencing (NGS) technologies has led to remarkable advances in whole genome sequencing, which provides ultra-throughput sequences to revolutionize plant genotyping and breeding. To further broaden NGS usages to large crop genomes such as maize and wheat, genotyping-by-sequencing (GBS) has been developed and applied in sequencing multiplexed samples that combine molecular marker discovery and genotyping. GBS is a novel application of NGS protocols for discovering and genotyping SNPs in crop genomes and populations. The GBS approach includes the digestion of genomic DNA with restriction enzymes followed by the ligation of barcode adapter, PCR amplification and sequencing of the amplified DNA pool on a single lane of flow cells. Bioinformatic pipelines are needed to analyze and interpret GBS datasets. As an ultimate MAS tool and a cost-effective technique, GBS has been successfully used in implementing genome-wide association study (GWAS), genomic diversity study, genetic linkage analysis, molecular marker discovery and genomic selection under a large scale of plant breeding programs.

Finding and mapping new genes faster than ever: revisited

This article recounts some of the early days of the Human Genome Project, covering the important and sometimes controversial role that complementary DNA-based approaches played in the discovery and mapping of the majority of human genes. It also describes my involvement in this effort and my lab's development of methods for rapid sequence identification and mapping of human genes.

Association of eight EST-derived SNPs with carcass and meat quality traits in pigs

The identification of genetic markers associated with important economic traits is fundamental to improving the productivity and quality of livestock. In this investigation, we searched for 177 expressed sequence tags (ESTs) putatively involved in meat quality from the available pig EST database, and detected eight single nucleotide polymorphisms (SNPs) in eight ESTs. We investigated the associations of these SNPs with 18 carcass and meat quality traits in a Landrace × Lantang F2 resource population (n = 257). Association analysis revealed that seven SNPs (except E42) were associated with some of the carcass- and meat quality-related traits. Particularly, significant associations of three SNPs (E53, E82, and E36) with backfat thickness traits were observed. Further, the genetic effects of E53 on four live backfat thickness traits were validated in an independent population (n = 221). More investigations about E53 sequence characteristics were performed, i.e., radiation hybrid (RH) mapping, 3'-RACE, and screening analysis of the positive BAC clones. Our research identified the genetic effects of eight EST-derived SNPs on carcass and meat quality traits, and suggested that E53 may be a useful marker for live backfat thickness traits in pig breeding programs.

Transcriptome analysis of a petal anthocyanin polymorphism in the arctic mustard, Parrya nudicaulis

Angiosperms are renown for their diversity of flower colors. Often considered adaptations to pollinators, the most common underlying pigments, anthocyanins, are also involved in plants’ stress response. Although the anthocyanin biosynthetic pathway is well characterized across many angiosperms and is composed of a few candidate genes, the consequences of blocking this pathway and producing white flowers has not been investigated at the transcriptome scale. We take a transcriptome-wide approach to compare expression differences between purple and white petal buds in the arctic mustard, Parrya nudicaulis, to determine which genes’ expression are consistently correlated with flower color. Using mRNA-Seq and de novo transcriptome assembly, we assembled an average of 722 bp per gene (49.81% coding sequence based on the A. thaliana homolog) for 12,795 genes from the petal buds of a pair of purple and white samples. Our results correlate strongly with qRT-PCR analysis of nine candidate genes in the anthocyanin biosynthetic pathway where chalcone synthase has the greatest difference in expression between color morphs (P/W = ∼7×). Among the most consistently differentially expressed genes between purple and white samples, we found 3× more genes with higher expression in white petals than in purple petals. These include four unknown genes, two drought-response genes (CDSP32, ERD5), a cold-response gene (GR-RBP2), and a pathogen defense gene (DND1). Gene ontology analysis of the top 2% of genes with greater expression in white relative to purple petals revealed enrichment in genes associated with stress responses including cold, drought and pathogen defense. Unlike the uniform downregulation of chalcone synthase that may be directly involved in the loss of petal anthocyanins, the variable expression of several genes with greater expression in white petals suggest that the physiological and ecological consequences of having white petals may be microenvironment-dependent.

Generation of expressed sequence tags under cadmium stress for gene discovery and development of molecular markers in chickpea

Chickpea is the world's third most important legume crop and belongs to Fabaceae family but suffered from severe yield loss due to various biotic and abiotic stresses. Development of modern genomic tools such as molecular markers and identification of resistant genes associated with these stresses facilitate improvement in chickpea breeding towards abiotic stress tolerance. In this study, 1597 high-quality expressed sequence tags (ESTs) were generated from a cDNA library of variety Pusa 1105 root tissue after cadmium (Cd) treatment. Assembly of ESTs resulted in a total of 914 unigenes of which putative homology was obtained for 38.8 % of unigenes after BLASTX search. In terms of species distribution, majority of sequences found similarity with Medicago truncatula followed by Glycine max, Vitis vinifera and Populus trichocarpa and Pisum sativum sequences. Functional annotation was assigned using Blast2Go, and the Gene Ontology (GO) terms were categorized into biological process, molecular function and cellular component. Approximately 10.83 % of unigenes were assigned at least one GO term. Moreover, in the distribution of transcripts into various biological pathways, 20 of the annotated transcripts were assigned to ten pathways in KEGG database. A majority of the genes were found to be involved in sulphur and nitrogen metabolism. In the quantitative real-time PCR analysis, five of the transcription factors and three of the transporter genes were found to be highly expressed after Cd treatment. Besides, the utility of ESTs was demonstrated by exploiting them for the development of 83 genic molecular markers including EST-simple sequence repeats and intron targeted polymorphism that would assist in tagging of genes related to metal stress for future prospects.

SeeSite: Characterizing Relationships between Splice Junctions and Splicing Enhancers

RNA splicing is a cellular process driven by the interaction between numerous regulatory sequences and binding sites, however, such interactions have been primarily explored by laboratory methods since computational tools largely ignore the relationship between different splicing elements. Current computational methods identify either splice sites or other regulatory sequences, such as enhancers and silencers. We present a novel approach for characterizing co-occurring relationships between splice site motifs and splicing enhancers. Our approach relies on an efficient algorithm for approximately solving Consensus Sequence with Outliers , an NP-complete string clustering problem. In particular, we give an algorithm for this problem that outputs near-optimal solutions in polynomial time. To our knowledge, this is the first formulation and computational attempt for detecting co-occurring sequence elements in RNA sequence data. Further, we demonstrate that SeeSite is capable of showing that certain ESEs are preferentially associated with weaker splice sites, and that there exists a co-occurrence relationship with splice site motifs.

EXPRSS: an Illumina based high-throughput expression-profiling method to reveal transcriptional dynamics

Background

Next Generation Sequencing technologies have facilitated differential gene expression analysis through RNA-seq and Tag-seq methods. RNA-seq has biases associated with transcript lengths, lacks uniform coverage of regions in mRNA and requires 10–20 times more reads than a typical Tag-seq. Most existing Tag-seq methods either have biases or not high throughput due to use of restriction enzymes or enzymatic manipulation of 5’ ends of mRNA or use of RNA ligations.

Results

We have developed EXpression Profiling through Randomly Sheared cDNA tag Sequencing (EXPRSS) that employs acoustic waves to randomly shear cDNA and generate sequence tags at a relatively defined position (~150-200 bp) from the 3′ end of each mRNA. Implementation of the method was verified through comparative analysis of expression data generated from EXPRSS, NlaIII-DGE and Affymetrix microarray and through qPCR quantification of selected genes. EXPRSS is a strand specific and restriction enzyme independent tag sequencing method that does not require cDNA length-based data transformations. EXPRSS is highly reproducible, is high-throughput and it also reveals alternative polyadenylation and polyadenylated antisense transcripts. It is cost-effective using barcoded multiplexing, avoids the biases of existing SAGE and derivative methods and can reveal polyadenylation position from paired-end sequencing.

Conclusions

EXPRSS Tag-seq provides sensitive and reliable gene expression data and enables high-throughput expression profiling with relatively simple downstream analysis.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-341) contains supplementary material, which is available to authorized users.

Lost in Translation (LiT): IUPHAR Review 6

Translational medicine is a roller coaster with occasional brilliant successes and a large majority of failures. Lost in Translation 1 ('LiT1'), beginning in the 1950s, was a golden era built upon earlier advances in experimental physiology, biochemistry and pharmacology, with a dash of serendipity, that led to the discovery of many new drugs for serious illnesses. LiT2 saw the large-scale industrialization of drug discovery using high-throughput screens and assays based on affinity for the target molecule. The links between drug development and university sciences and medicine weakened, but there were still some brilliant successes. In LiT3, the coverage of translational medicine expanded from molecular biology to drug budgets, with much greater emphasis on safety and official regulation. Compared with R&D expenditure, the number of breakthrough discoveries in LiT3 was disappointing, but monoclonal antibodies for immunity and inflammation brought in a new golden era and kinase inhibitors such as imatinib were breakthroughs in cancer. The pharmaceutical industry is trying to revive the LiT1 approach by using phenotypic assays and closer links with academia. LiT4 faces a data explosion generated by the genome project, GWAS, ENCODE and the 'omics' that is in danger of leaving LiT4 in a computerized cloud. Industrial laboratories are filled with masses of automated machinery while the scientists sit in a separate room viewing the results on their computers. Big Data will need Big Thinking in LiT4 but with so many unmet medical needs and so many new opportunities being revealed there are high hopes that the roller coaster will ride high again.

Characterization of mango (Mangifera indica L.) transcriptome and chloroplast genome

We characterized mango leaf transcriptome and chloroplast genome using next generation DNA sequencing. The RNA-seq output of mango transcriptome generated >12 million reads (total nucleotides sequenced >1 Gb). De novo transcriptome assembly generated 30,509 unigenes with lengths in the range of 300 to ≥3,000 nt and 67× depth of coverage. Blast searching against nonredundant nucleotide databases and several Viridiplantae genomic datasets annotated 24,593 mango unigenes (80% of total) and identified Citrus sinensis as closest neighbor of mango with 9,141 (37%) matched sequences. The annotation with gene ontology and Clusters of Orthologous Group terms categorized unigene sequences into 57 and 25 classes, respectively. More than 13,500 unigenes were assigned to 293 KEGG pathways. Besides major plant biology related pathways, KEGG based gene annotation pointed out active presence of an array of biochemical pathways involved in (a) biosynthesis of bioactive flavonoids, flavones and flavonols, (b) biosynthesis of terpenoids and lignins and (c) plant hormone signal transduction. The mango transcriptome sequences revealed 235 proteases belonging to five catalytic classes of proteolytic enzymes. The draft genome of mango chloroplast (cp) was obtained by a combination of Sanger and next generation sequencing. The draft mango cp genome size is 151,173 bp with a pair of inverted repeats of 27,093 bp separated by small and large single copy regions, respectively. Out of 139 genes in mango cp genome, 91 found to be protein coding. Sequence analysis revealed cp genome of C. sinensis as closest neighbor of mango. We found 51 short repeats in mango cp genome supposed to be associated with extensive rearrangements. This is the first report of transcriptome and chloroplast genome analysis of any Anacardiaceae family member.

CancerEST: a web-based tool for automatic meta-analysis of public EST data

The identification of cancer-restricted biomarkers is fundamental to the development of novel cancer therapies and diagnostic tools. The construction of comprehensive profiles to define tissue- and cancer-specific gene expression has been central to this. To this end, the exploitation of the current wealth of 'omic'-scale databases can be facilitated by automated approaches, allowing researchers to directly address specific biological questions. Here we present CancerEST, a user-friendly and intuitive web-based tool for the automated identification of candidate cancer markers/targets, for examining tissue specificity as well as for integrated expression profiling. CancerEST operates by means of constructing and meta-analyzing expressed sequence tag (EST) profiles of user-supplied gene sets across an EST database supporting 36 tissue types. Using a validation data set from the literature, we show the functionality and utility of CancerEST. DATABASE URL: http://www.cancerest.org.uk.

A reference pan-genome approach to comparative bacterial genomics: identification of novel epidemiological markers in pathogenic Campylobacter

The increasing availability of hundreds of whole bacterial genomes provides opportunities for enhanced understanding of the genes and alleles responsible for clinically important phenotypes and how they evolved. However, it is a significant challenge to develop easy-to-use and scalable methods for characterizing these large and complex data and relating it to disease epidemiology. Existing approaches typically focus on either homologous sequence variation in genes that are shared by all isolates, or non-homologous sequence variation--focusing on genes that are differentially present in the population. Here we present a comparative genomics approach that simultaneously approximates core and accessory genome variation in pathogen populations and apply it to pathogenic species in the genus Campylobacter. A total of 7 published Campylobacter jejuni and Campylobacter coli genomes were selected to represent diversity across these species, and a list of all loci that were present at least once was compiled. After filtering duplicates a 7-isolate reference pan-genome, of 3,933 loci, was defined. A core genome of 1,035 genes was ubiquitous in the sample accounting for 59% of the genes in each isolate (average genome size of 1.68 Mb). The accessory genome contained 2,792 genes. A Campylobacter population sample of 192 genomes was screened for the presence of reference pan-genome loci with gene presence defined as a BLAST match of ≥ 70% identity over ≥ 50% of the locus length--aligned using MUSCLE on a gene-by-gene basis. A total of 21 genes were present only in C. coli and 27 only in C. jejuni, providing information about functional differences associated with species and novel epidemiological markers for population genomic analyses. Homologs of these genes were found in several of the genomes used to define the pan-genome and, therefore, would not have been identified using a single reference strain approach.

Strategies for measurement of biotransformation enzyme gene expression

The analysis of gene expression is an integral part of any gene function research. A wide variety of techniques have been developed for this purpose, each with its own advantages and limitations. The following chapter seeks to provide an overview of some of the most recent as well as conventional methods to study gene expression. These approaches include Northern blot analysis, ribonuclease protection assay, reverse transcription polymerase chain reaction, expressed tag sequencing, differential display, cDNA arrays, serial analysis of gene expression, and transcriptome sequencing. The current applications of the information derived from gene expression studies require most of the assays to be adaptable for the quantitative analysis of a large number of samples and endpoints within a short period of time coupled with cost-effectiveness. A comparison of some of these features of each analytical approach as well as their advantages and disadvantages has also been provided.

RNA-seq analysis of transcriptome and glucosinolate metabolism in seeds and sprouts of broccoli (Brassica oleracea var. italic)

BACKGROUND

Broccoli (Brassica oleracea var. italica), a member of Cruciferae, is an important vegetable containing high concentration of various nutritive and functional molecules especially the anticarcinogenic glucosinolates. The sprouts of broccoli contain 10-100 times higher level of glucoraphanin, the main contributor of the anticarcinogenesis, than the edible florets. Despite the broccoli sprouts' functional importance, currently available genetic and genomic tools for their studies are very limited, which greatly restricts the development of this functionally important vegetable.

RESULTS

A total of ∼85 million 251 bp reads were obtained. After de novo assembly and searching the assembled transcripts against the Arabidopsis thaliana and NCBI nr databases, 19,441 top-hit transcripts were clustered as unigenes with an average length of 2,133 bp. These unigenes were classified according to their putative functional categories. Cluster analysis of total unigenes with similar expression patterns and differentially expressed unigenes among different tissues, as well as transcription factor analysis were performed. We identified 25 putative glucosinolate metabolism genes sharing 62.04-89.72% nucleotide sequence identity with the Arabidopsis orthologs. This established a broccoli glucosinolate metabolic pathway with high colinearity to Arabidopsis. Many of the biosynthetic and degradation genes showed higher expression after germination than in seeds; especially the expression of the myrosinase TGG2 was 20-130 times higher. These results along with the previous reports about these genes' studies in Arabidopsis and the glucosinolate concentration in broccoli sprouts indicate the breakdown products of glucosinolates may play important roles in the stage of broccoli seed germination and sprout development.

CONCLUSION

Our study provides the largest genetic resource of broccoli to date. These data will pave the way for further studies and genetic engineering of broccoli sprouts and will also provide new insight into the genomic research of this species and its relatives.

Approaches for recognizing disease genes based on network

Diseases are closely related to genes, thus indicating that genetic abnormalities may lead to certain diseases. The recognition of disease genes has long been a goal in biology, which may contribute to the improvement of health care and understanding gene functions, pathways, and interactions. However, few large-scale gene-gene association datasets, disease-disease association datasets, and gene-disease association datasets are available. A number of machine learning methods have been used to recognize disease genes based on networks. This paper states the relationship between disease and gene, summarizes the approaches used to recognize disease genes based on network, analyzes the core problems and challenges of the methods, and outlooks future research direction.

Improving mRNA 5' coding sequence determination in the mouse genome

The incomplete determination of the mRNA 5' end sequence may lead to the incorrect assignment of the first AUG codon and to errors in the prediction of the encoded protein product. Due to the significance of the mouse as a model organism in biomedical research, we performed a systematic identification of coding regions at the 5' end of all known mouse mRNAs, using an automated expressed sequence tag (EST)-based approach which we have previously described. By parsing almost 4 million BLAT alignments we found 351 mouse loci, out of 20,221 analyzed, in which an extension of the mRNA 5' coding region was identified. Proof-of-concept confirmation was obtained by in vitro cloning and sequencing for Apc2 and Mknk2 cDNAs. We also generated a list of 16,330 mouse mRNAs where the presence of an in-frame stop codon upstream of the known start codon indicates completeness of the coding sequence at 5' end in the current form. Systematic searches in the main mouse genome databases and genome browsers showed that 82% of our results are original and have not been identified by their annotation pipelines. Moreover, the same information is not easily derivable from RNA-Seq data, due to short sequence length and laboriousness in building full-length transcript structures. In conclusion, our results improve the determination of full-length 5' coding sequences and might be useful in order to reduce errors when studying mouse gene structure and function in biomedical research.

Analyses of hypomethylated oil palm gene space

Demand for palm oil has been increasing by an average of ∼8% the past decade and currently accounts for about 59% of the world's vegetable oil market. This drives the need to increase palm oil production. Nevertheless, due to the increasing need for sustainable production, it is imperative to increase productivity rather than the area cultivated. Studies on the oil palm genome are essential to help identify genes or markers that are associated with important processes or traits, such as flowering, yield and disease resistance. To achieve this, 294,115 and 150,744 sequences from the hypomethylated or gene-rich regions of Elaeis guineensis and E. oleifera genome were sequenced and assembled into contigs. An additional 16,427 shot-gun sequences and 176 bacterial artificial chromosomes (BAC) were also generated to check the quality of libraries constructed. Comparison of these sequences revealed that although the methylation-filtered libraries were sequenced at low coverage, they still tagged at least 66% of the RefSeq supported genes in the BAC and had a filtration power of at least 2.0. A total 33,752 microsatellites and 40,820 high-quality single nucleotide polymorphism (SNP) markers were identified. These represent the most comprehensive collection of microsatellites and SNPs to date and would be an important resource for genetic mapping and association studies. The gene models predicted from the assembled contigs were mined for genes of interest, and 242, 65 and 14 oil palm transcription factors, resistance genes and miRNAs were identified respectively. Examples of the transcriptional factors tagged include those associated with floral development and tissue culture, such as homeodomain proteins, MADS, Squamosa and Apetala2. The E. guineensis and E. oleifera hypomethylated sequences provide an important resource to understand the molecular mechanisms associated with important agronomic traits in oil palm.

Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding

Marker-assisted selection (MAS) refers to the use of molecular markers to assist phenotypic selections in crop improvement. Several types of molecular markers, such as single nucleotide polymorphism (SNP), have been identified and effectively used in plant breeding. The application of next-generation sequencing (NGS) technologies has led to remarkable advances in whole genome sequencing, which provides ultra-throughput sequences to revolutionize plant genotyping and breeding. To further broaden NGS usages to large crop genomes such as maize and wheat, genotyping-by-sequencing (GBS) has been developed and applied in sequencing multiplexed samples that combine molecular marker discovery and genotyping. GBS is a novel application of NGS protocols for discovering and genotyping SNPs in crop genomes and populations. The GBS approach includes the digestion of genomic DNA with restriction enzymes followed by the ligation of barcode adapter, PCR amplification and sequencing of the amplified DNA pool on a single lane of flow cells. Bioinformatic pipelines are needed to analyze and interpret GBS datasets. As an ultimate MAS tool and a cost-effective technique, GBS has been successfully used in implementing genome-wide association study (GWAS), genomic diversity study, genetic linkage analysis, molecular marker discovery and genomic selection under a large scale of plant breeding programs.

Characterizing multi-omic data in systems biology

In today's biology, studies have shifted to analyzing systems over discrete biochemical reactions and pathways. These studies depend on combining the results from scores of experimental methods that analyze DNA; mRNA; noncoding RNAs, DNA, RNA, and protein interactions; and the nucleotide modifications that form the epigenome into global datasets that represent a diverse array of "omics" data (transcriptional, epigenetic, proteomic, metabolomic). The methods used to collect these data consist of high-throughput data generation platforms that include high-content screening, imaging, flow cytometry, mass spectrometry, and nucleic acid sequencing. Of these, the next-generation DNA sequencing platforms predominate because they provide an inexpensive and scalable way to quickly interrogate the molecular changes at the genetic, epigenetic, and transcriptional level. Furthermore, existing and developing single-molecule sequencing platforms will likely make direct RNA and protein measurements possible, thus increasing the specificity of current assays and making it possible to better characterize "epi-alterations" that occur in the epigenome and epitranscriptome. These diverse data types present us with the largest challenge: how do we develop software systems and algorithms that can integrate these datasets and begin to support a more democratic model where individuals can capture and track their own medical information through biometric devices and personal genome sequencing? Such systems will need to provide the necessary user interactions to work with the trillions of data points needed to make scientific discoveries. Here, we describe novel approaches in the genesis and processing of such data, models to integrate these data, and the increasing ubiquity of self-reporting and self-measured genomics and health data.

Genomics and bioinformatics resources for translational science in Rosaceae

Recent technological advances in biology promise unprecedented opportunities for rapid and sustainable advancement of crop quality. Following this trend, the Rosaceae research community continues to generate large amounts of genomic, genetic and breeding data. These include annotated whole genome sequences, transcriptome and expression data, proteomic and metabolomic data, genotypic and phenotypic data, and genetic and physical maps. Analysis, storage, integration and dissemination of these data using bioinformatics tools and databases are essential to provide utility of the data for basic, translational and applied research. This review discusses the currently available genomics and bioinformatics resources for the Rosaceae family.

The road from next-generation sequencing to personalized medicine

Moving from a traditional medical model of treating pathologies to an individualized predictive and preventive model of personalized medicine promises to reduce the healthcare cost on an overburdened and overwhelmed system. Next-generation sequencing (NGS) has the potential to accelerate the early detection of disorders and the identification of pharmacogenetics markers to customize treatments. This review explains the historical facts that led to the development of NGS along with the strengths and weakness of NGS, with a special emphasis on the analytical aspects used to process NGS data. There are solutions to all the steps necessary for performing NGS in the clinical context where the majority of them are very efficient, but there are some crucial steps in the process that need immediate attention.

The filamentous fungus Sordaria macrospora as a genetic model to study fruiting body development

Filamentous fungi are excellent experimental systems due to their short life cycles as well as easy and safe manipulation in the laboratory. They form three-dimensional structures with numerous different cell types and have a long tradition as genetic model organisms used to unravel basic mechanisms underlying eukaryotic cell differentiation. The filamentous ascomycete Sordaria macrospora is a model system for sexual fruiting body (perithecia) formation. S. macrospora is homothallic, i.e., self-fertile, easily genetically tractable, and well suited for large-scale genomics, transcriptomics, and proteomics studies. Specific features of its life cycle and the availability of a developmental mutant library make it an excellent system for studying cellular differentiation at the molecular level. In this review, we focus on recent developments in identifying gene and protein regulatory networks governing perithecia formation. A number of tools have been developed to genetically analyze developmental mutants and dissect transcriptional profiles at different developmental stages. Protein interaction studies allowed us to identify a highly conserved eukaryotic multisubunit protein complex, the striatin-interacting phosphatase and kinase complex and its role in sexual development. We have further identified a number of proteins involved in chromatin remodeling and transcriptional regulation of fruiting body development. Furthermore, we review the involvement of metabolic processes from both primary and secondary metabolism, and the role of nutrient recycling by autophagy in perithecia formation. Our research has uncovered numerous players regulating multicellular development in S. macrospora. Future research will focus on mechanistically understanding how these players are orchestrated in this fungal model system.

Novel alternative splicing isoform biomarkers identification from high-throughput plasma proteomics profiling of breast cancer

BACKGROUND

In the biopharmaceutical industry, biomarkers define molecular taxonomies of patients and diseases and serve as surrogate endpoints in early-phase drug trials. Molecular biomarkers can be much more sensitive than traditional lab tests. Discriminating disease biomarkers by traditional method such as DNA microarray has proved challenging. Alternative splicing isoform represents a new class of diagnostic biomarkers. Recent scientific evidence is demonstrating that the differentiation and quantification of individual alternative splicing isoforms could improve insights into disease diagnosis and management. Identifying and characterizing alternative splicing isoforms are essential to the study of molecular mechanisms and early detection of complex diseases such as breast cancer. However, there are limitations with traditional methods used for alternative splicing isoform determination such as transcriptome-level, low level of coverage and poor focus on alternative splicing.

RESULTS

Therefore, we presented a peptidomics approach to searching novel alternative splicing isoforms in clinical proteomics. Our results showed that the approach has significant potential in enabling discovery of new types of high-quality alternative splicing isoform biomarkers.

CONCLUSIONS

We developed a peptidomics approach for the proteomics community to analyze, identify, and characterize alternative splicing isoforms from MS-based proteomics experiments with more coverage and exclusive focus on alternative splicing. The approach can help generate novel hypotheses on molecular risk factors and molecular mechanisms of cancer in early stage, leading to identification of potentially highly specific alternative splicing isoform biomarkers for early detection of cancer.