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Rice growth and development is highly affected by the excessive concentration of the heavy metal cadmium (Cd). To elucidate the molecular basis of Cd tolerance, we carried out comprehensive transcriptome profiling of diverse groups of genes like those encoding antioxidative enzymes (APX, CAT, SOD, and GR), osmolyte-regulatory enzymes (P5CS, PDH, and BADH1), polyamine-regulatory enzymes (SAMDC, SPDS, SPMS, and DAO), transcription factors (TRAB-1 and WRKY-71), Osem and RbcS from leaf and root tissues under cadmium chloride (1.5 mM) stress at different time points (6, 12, and 24 h) of exposure in two indica rice varieties, that is, IR-64 (sensitive) and Nonabokra (tolerant). Analyses of massively parallel signature sequencing (MPSS) and publicly available microarray databases indicated the physiological role of the concerned genes and possible cross-talk between Cd and other abiotic stresses. Moreover, the pattern of expression, for a particular gene in leaves, varied in most cases from that in roots, suggesting a tissue-specific response. The higher accumulation of TRAB-1 transcript and protein in the tolerant cultivar Nonabokra in response to Cd stress suggested a role for TRAB-1 as a nodal component in Cd signaling pathway. Overall, the transcriptome analyses undertaken in the present study indicated that the genes, belonging to different functional classes and conferring Cd tolerance, were differentially regulated and mostly overrepresented under stress situations, thus providing novel insight into the functional basis of Cd tolerance in rice varieties and the importance of cross-talk of different signaling pathways. The present work will also pave the way in the future to select gene(s) for overexpression, so that rice can better tolerate Cd stress.

RNA sequencing (RNA-seq) has become a standard procedure to investigate transcriptional changes between conditions and is routinely used in research and clinics. While standard differential expression (DE) analysis between two conditions has been extensively studied, and improved over the past decades, RNA-seq time course (TC) DE analysis algorithms are still in their early stages. In this study, we compare, for the first time, existing TC RNA-seq tools on an extensive simulation data set and validated the best performing tools on published data. Surprisingly, TC tools were outperformed by the classical pairwise comparison approach on short time series (, Briefings in Bioinformatics, 20 (1), ISSN:1467-5463, ISSN:1477-4054

Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-seq data. We studied the effects of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 single cells from 8 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (50 bp, while it failed for datasets with

Lung adenocarcinoma is one of the leading causes of cancer-related death worldwide. We showed transcriptomic profiles in three pairs of tumors and adjacent non-tumor lung tissues using next-generation sequencing (NGS) to screen protein-coding RNAs and microRNAs. Combined with meta-analysis from the Oncomine and Gene Expression Omnibus (GEO) databases, we identified a representative genetic expression signature in lung adenocarcinoma. There were 9 upregulated genes, and 8 downregulated genes in lung adenocarcinoma. The analysis of the effects from each gene expression on survival outcome indicated that 6 genes (AGR2, SPDEF, CDKN2A, CLDN3, SFN, and PHLDA2) play oncogenic roles, and 7 genes (PDK4, FMO2, CPED1, GNG11, IL33, BTNL9, and FABP4) act as tumor suppressors in lung adenocarcinoma. In addition, we also identified putative genetic interactions, in which there were 5 upregulated microRNAs with specific targets - hsa-miR-183-5p-BTNL9, hsa-miR-33b-5p-CPED1, hsa-miR-429-CPED1, hsa-miR-182-5p-FMO2, and hsa-miR-130b-5p-IL33. These 5 microRNAs have been shown to be associated with tumorigenesis in lung cancer. Our findings suggest that these genetic interactions play important roles in the progression of lung adenocarcinoma. We propose that this molecular change of genetic expression may represent a novel signature in lung adenocarcinoma, which may be developed for diagnostic and therapeutic strategies in the future.

© 2017 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 12 month embargo from date of publication (July 2017) in accordance with the publisher’s archiving policy, Accurate sequencing of the control region of the mitochondrial genome is notoriously difficult due to the presence of polycytosine bases, termed C-tracts. The precise number of bases that constitute a C-tract and the bases beyond the poly cytosines may not be accurately defined when analyzing Sanger sequencing data separated by capillary electrophoresis. Massively parallel sequencing has the potential to resolve such poor definition and provides the opportunity to discover variants due to length heteroplasmy. In this study, the control region of mitochondrial genomes from 20 samples was sequenced using both standard Sanger methods with separation by capillary electrophoresis and also using massively parallel DNA sequencing technology. After comparison of the two sets of generated sequence, with the exception of the C-tracts where length heteroplasmy was observed, all sequences were concordant. Sequences of three segments 16184–16193, 303–315 and 568–573 with C-tracts in HVI, II and III can be clearly defined from the massively parallel sequencing data using the program SEQ Mapper. Multiple sequence variants were observed in the length of C-tracts longer than 7 bases. Our report illustrates the accurate designation of all the length variants leading to heteroplasmy in the control region of the mitochondrial genome that can be determined by SEQ Mapper based on data generated by massively parallel DNA sequencing.

We present a tool to measure gene and protein expression levels in single cells with DNA-labeled antibodies and droplet microfluidics. Using the RNA expression and protein sequencing assay (REAP-seq), we quantified proteins with 82 barcoded antibodies and >20,000 genes in a single workflow. We used REAP-seq to assess the costimulatory effects of a CD27 agonist on human CD8+ lymphocytes and to identify and characterize an unknown cell type.

Single nucleus RNA-seq (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but does not provide the throughput required to analyse many cells from complex tissues. Here, we develop DroNc-seq, massively parallel sNuc-Seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient and unbiased classification of cell types, paving the way for systematic charting of cell atlases.

Isolength restriction site-associated DNA (isoRAD) sequencing is a very simple but powerful approach that was originally developed for genome-wide genotyping at minimal labor and cost, and it has recently extended its applicability to allow quantification of DNA methylation levels. The isoRAD method is distinct from other genotyping-by-sequencing (GBS) methods because of its use of special restriction enzymes to produce isolength tags (32-36 bp), and sequencing of these uniform tags can bring many benefits. However, the relatively short tags produced by the original protocol are mostly suited to single-end (SE) sequencing (36-50 bp), and therefore they cannot efficiently match the gradually increased sequencing capacity of next-generation sequencing (NGS) platforms. To address this issue, we describe an advanced protocol that allows the preparation of five concatenated isoRAD tags for Illumina paired-end (PE) sequencing (100-150 bp). The configuration of the five concatenated tags is highly flexible, and can be defined by users to work with a desired combination of samples and/or restriction enzymes to suit specific research purposes. In comparison with the original protocol, the advanced protocol has an additional digestion and ligation step, and library preparation can be completed in ∼8 h.

We describe a patient with early onset severe axonal Charcot-Marie-Tooth disease (CMT2) with dominant inheritance, in whom Sanger sequencing failed to detect a mutation in the mitofusin 2 (MFN2) gene because of a single nucleotide polymorphism (rs2236057) under the PCR primer sequence. The severe early onset phenotype and the family history with severely affected mother (died after delivery) was very suggestive of CMT2A and this suspicion was finally confirmed by a MFN2 mutation. The mutation p.His361Tyr was later detected in the patient by massively parallel sequencing with a gene panel for hereditary neuropathies. According to this information, new primers for amplification and sequencing were designed which bind away from the polymorphic sites of the patient's DNA. Sanger sequencing with these new primers then confirmed the heterozygous mutation in the MFN2 gene in this patient. This case report shows that massively parallel sequencing may in some rare cases be more sensitive than Sanger sequencing and highlights the importance of accurate primer design which requires special attention.

Background MicroRNAs (miRNAs) are a class of small noncoding RNAs that regulate the expression of other genes. Several computational methods were developed to identify miRNA genes in Arabidopsis thaliana. However, none of these methods can identify conserved and non-conserved miRNAs while maintaining high sensitivity and specificity at the same time. In addition, the number of miRNA genes in plants remains unclear. Results We present a genome-wide computational approach (implemented in a set of scripts termed “miRscore”) to identify both conserved and non-conserved miRNA genes in Arabidopsis. When combined with experimental data from small RNA massively parallel signature sequencing and Northern blotting, our method captured 96 of 117 miRNA genes in version 8.0 of miRBase, including six genes not conserved in rice. In addition, we validated four new miRNA genes that are not conserved in rice, Populus, or Medicago. One of the predicted targets was validated by 5’ RNA ligase-mediated rapid amplification of cDNA ends. We estimate that the maximal number of Arabidopsis miRNA genes conserved in rice is around 137, while the number of Arabidopsis-specific miRNA genes is more variable and

Highlights • Sequencing results were in agreement for biologically conserved positions. • For biologically variable positions, sequencing depth impacted precision. • Results were biased by the algorithm used to align reads to the reference., This study presents the results from an interlaboratory sequencing study for which we developed a novel high-resolution method for comparing data from different sequencing platforms for a multi-copy, paralogous gene. The combination of PCR amplification and 16S ribosomal RNA gene (16S rRNA) sequencing has revolutionized bacteriology by enabling rapid identification, frequently without the need for culture. To assess variability between laboratories in sequencing 16S rRNA, six laboratories sequenced the gene encoding the 16S rRNA from Escherichia coli O157:H7 strain EDL933 and Listeria monocytogenes serovar 4b strain NCTC11994. Participants performed sequencing methods and protocols available in their laboratories: Sanger sequencing, Roche 454 pyrosequencing®, or Ion Torrent PGM®. The sequencing data were evaluated on three levels: (1) identity of biologically conserved position, (2) ratio of 16S rRNA gene copies featuring identified variants, and (3) the collection of variant combinations in a set of 16S rRNA gene copies. The same set of biologically conserved positions was identified for each sequencing method. Analytical methods using Bayesian and maximum likelihood statistics were developed to estimate variant copy ratios, which describe the ratio of nucleotides at each identified biologically variable position, as well as the likely set of variant combinations present in 16S rRNA gene copies. Our results indicate that estimated variant copy ratios at biologically variable positions were only reproducible for high throughput sequencing methods. Furthermore, the likely variant combination set was only reproducible with increased sequencing depth and longer read lengths. We also demonstrate novel methods for evaluating variable positions when comparing multi-copy gene sequence data from multiple laboratories generated using multiple sequencing technologies.

Analyses of gene expressions in single cells are important for understanding detailed biological phenomena. Here, a highly sensitive and accurate method by sequencing (called “bead-seq”) to obtain a whole gene expression profile for a single cell is proposed. A key feature of the method is to use a complementary DNA (cDNA) library on magnetic beads, which enables adding washing steps to remove residual reagents in a sample preparation process. By adding the washing steps, the next steps can be carried out under the optimal conditions without losing cDNAs. Error sources were carefully evaluated to conclude that the first several steps were the key steps. It is demonstrated that bead-seq is superior to the conventional methods for single-cell gene expression analyses in terms of reproducibility, quantitative accuracy, and biases caused during sample preparation and sequencing processes.

Excessive generation of reactive oxygen species (ROS) due to environmental stresses critically effects plant development and productivity. Plants efficiently detoxify ROS by both non-enzymatic and enzymatic mechanisms. Plant glutathione peroxidases (GPXs) are non-haeme thiol peroxidases that catalyze the reduction of H2O2 (or organic hydroperoxides) to water or the respective alcohols using reduced glutathione or thioredoxin. Genome-wide analysis of the known GPXs from rice and Arabidopsis genomes revealed their gene structure, conserved motifs, localization and tissue-specific and/or organ-specific expression profiles in response to various abiotic stresses. Among the eight genes that encoded GPX proteins from Arabidopsis, AtGPX3 showed two alternate spliced forms that spread over four chromosomes. Five genes encoded for rice GPX proteins, while OsGPX1 showed three spliced variants that were distributed on five chromosomes. Utilizing the publicly available microarray and massively parallel signature sequencing (MPSS) data, the GPXs revealed stress-responsive, tissue-specific and/or organ-specific expression profiles. Presence of important cis-regulatory elements analyzed in the GPX promoter sequences revealed their overlapping or specific responsiveness to different abiotic stresses. Co-expression data of Arabidopsis GPX genes suggested that various protein kinase family members and stress-responsive proteins co-expressed with the GPX proteins. Transcript profile of rice GPX genes by qRT-PCR validated their functional roles in signal transduction and stress pathways. Results revealed that plant GPXs play a crucial role in response to stress and significantly contribute towards their growth and development.

Combined transcriptome and whole genome sequencing of the same ultra-low input sample down to single cells is a rapidly evolving approach for the analysis of rare cells. Besides stem cells, rare cells originating from tissues like tumor or biopsies, circulating tumor cells and cells from early embryonic development are under investigation. Herein we describe a universal method applicable for the analysis of minute amounts of sample material (150 to 200 cells) derived from sub-colony structures from human embryonic stem cells. The protocol comprises the combined isolation and separate amplification of poly(A) mRNA and whole genome DNA followed by next generation sequencing. Here we present a detailed description of the method developed and an overview of the results obtained for RNA and whole genome sequencing of human embryonic stem cells, sequencing data is available in the Gene Expression Omnibus (GEO) database under accession number GSE69471.

Global transcriptional profiling is a powerful tool that can expose expression patterns to define cellular states or to identify genes with similar expression patterns. In recent years, transcriptome profiling has been widely used to understand the genetic regulation of a particular cell type. Transcriptome is defined as a full range of messenger Ribonucleic acid (RNA) molecule expressed by an organism. In other words a transcriptome represents the small percentage of genetic code that is transcribed into RNA molecules. It can offer valuable information on the significant biological processes behind the maintenance of the functionality of the cell. Transcriptomics provides fundaments for more definitively designed studies and guidance to select the genes for functional studies. The technology for the study of the transcriptome is not dependent on any prior knowledge of the genes expressed in the cells. However, with regards to the administration and interpretation of the enormous data provided by transcriptome profiling challenges remain.. Four methods have been reviewed here that is, Microarray technology, Serial Analysis of Gene Expression (SAGE), RNA sequencing (RNA-Seq) and Massively Parallel Signature Sequencing (MPSS). The use of these technologies to analyse the expressed transcripts in several prokaryotic and eukaryotic genomes has revealed the high complexity of transcriptomes.

14-3-3 isoforms were relatively less conserved at the C-terminal region across plant groups. Both Os 14-3-3f and Os 14-3-3g were inducible with differential gene expression levels under different abiotic stress and developmental stages in sensitive and tolerant indica rice cultivars as confirmed both at transcript and protein level. Plant 14-3-3s has been well characterized to function in several signaling pathways, biotic as well as abiotic stress and nutrient metabolism. We attempted comprehensive analysis of 14-3-3 genes in different plant lineages such as green algae (Chlamydomonas reinhardtii), moss (Physcomitrella patens) and lycophyte (Selaginella moellendorffii), dicot Arabidopsis thaliana and monocot Oryza sativa sub sp. japonica at the gene and protein level. Sequence alignment results revealed that 14-3-3 isoforms were evolutionarily conserved across all taxa with variable C-terminal end. Phylogenetic analysis indicated that the majority of 14-3-3 isoforms in rice belong to the non-epsilon group that clustered separately from the dicot group. Segmental duplication event played a significant role in the expansion of both, Arabidopsis and rice, 14-3-3 isoforms as revealed by synteny studies. In silico gene expression using Massive Parallel Signature Sequencing and microarray analysis revealed that 14-3-3 isoforms have variable expression in different tissue types and under different abiotic stress regime in Arabidopsis and japonica rice. Both, semi-quantitative and qPCR results, confirmed that Os14-3-3f and Os14-3-3g were inducible under abiotic stress in lamina and roots of indica rice and relatively higher under salinity and cold stress in Nonabokra, under dehydration stress in N-22 and under exogenous ABA in IR-29 usually after 3–6 h of treatment. Both, 14-3-3f and 14-3-3g, were highly expressed in flag leaves, stems and panicles and mature roots. These results were further confirmed by immunoblot analysis of rice cultivars using Os14-3-3f antibody generated from recombinant Os14-3-3f protein. The results provide the first comprehensive report of Os14-3-3 gene expression in indica rice cultivars which differ in tolerance to abiotic stress that might be useful for further research.

Single cell RNA sequencing (scRNA-seq) has shown great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant sub-populations of T cells, and notably the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, such as RNA library capture, cell quality, and sequencing output have been suggested to affect the quality of scRNA-seq data, but these factors have not been systematically examined.We studied the effect of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (50 bp.Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.

The identification of mutations that are present at low frequencies in clinical samples is an essential component of precision medicine. The development of molecular barcoding for next-generation sequencing has greatly enhanced the sensitivity of detecting such mutations by massively parallel sequencing. However, further improvements in specificity would be useful for a variety of applications. We herein describe a technology (BiSeqS) that can increase the specificity of sequencing by at least two orders of magnitude over and above that achieved with molecular barcoding and can be applied to any massively parallel sequencing instrument. BiSeqS employs bisulfite treatment to distinguish the two strands of molecularly barcoded DNA; its specificity arises from the requirement for the same mutation to be identified in both strands. Because no library preparation is required, the technology permits very efficient use of the template DNA as well as sequence reads, which are nearly all confined to the amplicons of interest. Such efficiency is critical for clinical samples, such as plasma, in which only tiny amounts of DNA are often available. We show here that BiSeqS can be applied to evaluate transversions, as well as small insertions or deletions, and can reliably detect one mutation among >10,000 wild-type molecules.

Differential gene and transcript expression pattern of human primary monocytes from healthy young subjects were profiled under different sequencing depths (50M, 100M, and 200M reads). The raw data consisted of 1.3 billion reads generated from RNA sequencing (RNA-Seq) experiments. A total of 17,657 genes and 75,392 transcripts were obtained at sequencing depth of 200M. Total splice junction reads showed an even more significant increase. Comparative analysis of the expression patterns of immune-related genes revealed a total of 217 differentially expressed (DE) protein-coding genes and 50 DE novel transcripts, in which 40 DE protein-coding genes were related to the immune system. At higher sequencing depth, more genes, known and novel transcripts were identified and larger proportion of reads were allowed to map across splice junctions. The results also showed that increase in sequencing depth has no effect on the sequence alignment.

Cyclophilins are a set of ubiquitous proteins present in all subcellular compartments, involved in a wide variety of cellular processes. Comparative bioinformatics analysis of the rice and Arabidopsis genomes led us to identify novel putative cyclophilin gene family members in both the genomes not reported previously. We grouped cyclophilin members with similar molecular weight and subtypes together in the phylogenetic tree which indicated their co-evolution in rice and Arabidopsis. We also characterized a rice cyclophilin gene, OsCyp2-P (Os02g0121300), isolated from a salinity-tolerant landrace, Pokkali. Publicly available massively parallel signature sequencing (MPSS) and microarray data, besides our quantitative real time PCR (qRT-PCR) data suggest that transcript abundance of OsCyp2-P is regulated under different stress conditions in a developmental and organ specific manner. Ectopic expression of OsCyp2-P imparted multiple abiotic stress tolerance to transgenic tobacco plants as evidenced by higher root length, shoot length, chlorophyll content, and K(+)/Na(+) ratio under stress conditions. Transgenic plants also showed reduced lipid peroxidase content, electrolyte leakage, and superoxide content under stress conditions suggesting better ion homeostasis than WT plants. Localization studies confirmed that OsCyp2-P is localized in both cytosol and nucleus, indicating its possible interaction with several other proteins. The overall results suggest the explicit role of OsCyp2-P in bestowing multiple abiotic stress tolerance at the whole plant level. OsCyp2-P operates via reactive oxygen species (ROS) scavenging and ion homeostasis and thus is a promising candidate gene for enhancing multiple abiotic stress tolerance in crop plants.

RNA sequencing (RNAseq) samples the majority of expressed genes infrequently, owing to the large size, complex splicing and wide dynamic range of eukaryotic transcriptomes. This results in sparse sequencing coverage that can hinder robust isoform assembly and quantification. RNA capture sequencing (CaptureSeq) addresses this challenge by using oligonucleotide probes to capture selected genes or regions of interest for targeted sequencing. Targeted RNAseq provides enhanced coverage for sensitive gene discovery, robust transcript assembly and accurate gene quantification. Here we describe a detailed protocol for all stages of RNA CaptureSeq, from initial probe design considerations and capture of targeted genes to final assembly and quantification of captured transcripts. Initial probe design and final analysis can take less than 1 d, whereas the central experimental capture stage requires ∼7 d.

Myeloid leukemias are highly diverse diseases and have been shown to be associated with microRNA (miRNA) expression aberrations. The present study involved an in-depth miRNome analysis of two human acute myeloid leukemia (AML) cell lines, HL-60 and THP-1, and one human chronic myeloid leukemia (CML) cell line, K562, via massively parallel signature sequencing. mRNA expression profiles of these cell lines that were established previously in our lab facilitated an integrative analysis of miRNA and mRNA expression patterns. miRNA expression profiling followed by differential expression analysis and target prediction suggested numerous miRNA signatures in AML and CML cell lines. Some miRNAs may act as either tumor suppressors or oncomiRs in AML and CML by targeting key genes in AML and CML pathways. Expression patterns of cell type-specific miRNAs could partially reflect the characteristics of K562, HL-60 and THP-1 cell lines, such as actin filament-based processes, responsiveness to stimulus and phagocytic activity. miRNAs may also regulate myeloid differentiation, since they usually suppress differentiation regulators. Our study provides a resource to further investigate the employment of miRNAs in human leukemia subtyping, leukemogenesis and myeloid development. In addition, the distinctive miRNA signatures may be potential candidates for the clinical diagnosis, prognosis and treatment of myeloid leukemias.

The over-expression of plant specific SnRK2 gene family members by hyperosmotic stress and some by abscisic acid is well established. In this report, we have analyzed the evolution of SnRK2 gene family in different plant lineages including green algae, moss, lycophyte, dicot and monocot. Our results provide some evidences to indicate that the natural selection pressure had considerable influence on cis-regulatory promoter region and coding region of SnRK2 members in Arabidopsis and Oryza independently through time. Observed degree of sequence/motif conservation amongst SnRK2 homolog in all the analyzed plant lineages strongly supported their inclusion as members of this family. The chromosomal distributions of duplicated SnRK2 members have also been analyzed in Arabidopsis and Oryza. Massively Parallel Signature Sequencing (MPSS) database derived expression data and the presence of abiotic stress related promoter elements within the 1 kb upstream promoter region of these SnRK2 family members further strengthen the observations of previous workers. Additionally, the phylogenetic relationships of SnRK2 have been studied in all plant lineages along with their respective exon-intron structural patterns. Our results indicate that the ancestral SnRK2 gene of land plants gradually evolved by duplication and diversification and modified itself through exon-intron loss events to survive under environmental stress conditions.

Chickens (Gallus gallus) were the first avian species selected for whole genome sequencing because of their economic value, use as a food source, livelihood security and research importance. Any living organism contains a galaxy of genes which express all the phenotypes or characters by encoding proteins and peptides, and playing regulatory roles in the biological system. Functional genomics in turn, is a multidisciplinary approach to identify and demonstrate the functional roles of genes and other regulatory molecules such as microRNA and CpG methylation in biological pathways. In the last two decades, the chicken genome database has made significant advancements in accruing large amounts of genomic information through employing advanced bio-informatic tools. Several techniques such as cDNA microarray, serial analysis of gene expression, massively parallel signature sequencing, cDNA subtractive hybridisation and next generation sequencing have been utilised to investigate the genome-wide expression profile instead of revealing expression pattern of one or a few genes in various avian species. Expressed sequence tag or cDNA sequences are the key factors for identification of novel genes and understanding the complex molecular cascades of ontology. A large-scale cDNA library has been constructed from embryonic and adult tissues and consequently identified the presence of about 19,000 functional genes in chickens. The micro RNAs play crucial role in gene expression and to date, approximately 496 micro RNAs have been characterised. The non-coding RNA alters gene expression involved in cellular process, by modulating the chromatin architecture, transcription, RNA splicing, editing, translation and turnover. Functional genomics studies have been extensively used to identify genes associated with several production traits, immuno-genetic mechanism, host-pathogen interaction, pathogen biology etc. Nutrigenomics have determined the genomic mechanism involved in feed utilisation, metabolism and cholesterol synthesis etc., which ultimately reveal potential applications for improving the nutritional efficiency of birds. This review discusses the tools and utility of functional genomics approaches in chicken.

Emerging methods for the accurate quantification of gene expression in individual cells hold promise for revealing the extent, function and origins of cell-to-cell variability. Different high-throughput methods for single-cell RNA-seq have been introduced that vary in coverage, sensitivity and multiplexing ability. We recently introduced Smart-seq for transcriptome analysis from single cells, and we subsequently optimized the method for improved sensitivity, accuracy and full-length coverage across transcripts. Here we present a detailed protocol for Smart-seq2 that allows the generation of full-length cDNA and sequencing libraries by using standard reagents. The entire protocol takes ∼2 d from cell picking to having a final library ready for sequencing; sequencing will require an additional 1-3 d depending on the strategy and sequencer. The current limitations are the lack of strand specificity and the inability to detect nonpolyadenylated (polyA(-)) RNA.

Motivation: RNA-seq is replacing microarrays as the primary tool for gene expression studies. Many RNA-seq studies have used insufficient biological replicates, resulting in low statistical power and inefficient use of sequencing resources. Results: We show the explicit trade-off between more biological replicates and deeper sequencing in increasing power to detect differentially expressed (DE) genes. In the human cell line MCF7, adding more sequencing depth after 10 M reads gives diminishing returns on power to detect DE genes, whereas adding biological replicates improves power significantly regardless of sequencing depth. We also propose a cost-effectiveness metric for guiding the design of large-scale RNA-seq DE studies. Our analysis showed that sequencing less reads and performing more biological replication is an effective strategy to increase power and accuracy in large-scale differential expression RNA-seq studies, and provided new insights into efficient experiment design of RNA-seq studies. Availability and implementation: The code used in this paper is provided on: http://home.uchicago.edu/∼jiezhou/replication/. The expression data is deposited in the Gene Expression Omnibus under the accession ID GSE51403. Contact: kpwhite@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online.

High-throughput RNA sequencing (RNA-seq) is considered a powerful tool for novel gene discovery and fine-tuned transcriptional profiling. The digital nature of RNA-seq is also believed to simplify meta-analysis and to reduce background noise associated with hybridization-based approaches. The development of multiplex sequencing enables efficient and economic parallel analysis of gene expression. In addition, RNA-seq is of particular value when low RNA expression or modest changes between samples are monitored. However, recent data uncovered severe bias in the sequencing of small non-protein coding RNA (small RNA-seq or sRNA-seq), such that the expression levels of some RNAs appeared to be artificially enhanced and others diminished or even undetectable. The use of different adapters and barcodes during ligation as well as complex RNA structures and modifications drastically influence cDNA synthesis efficacies and exemplify sources of bias in deep sequencing. In addition, variable specific RNA G/C-content is associated with unequal polymerase chain reaction amplification efficiencies. Given the central importance of RNA-seq to molecular biology and personalized medicine, we review recent findings that challenge small non-protein coding RNA-seq data and suggest approaches and precautions to overcome or minimize bias.

Drought, salt and cold are the major abiotic stresses that limit the rice production. Identification of the key functional and regulatory genes in the abiotic stress signaling pathways is important for understanding the molecular basis of abiotic stress tolerance. In this study, we investigated the transcriptomes of rice leaves and roots under cold, drought, and salt stresses using the massively parallel signature sequencing (MPSS) and sequencing by synthesis (SBS) technologies. About 1.8 to 2.6 million individual signatures were obtained from the seven abiotic-stressed and control libraries of the japonica cultivar Nipponbare. A total of 102,630 and 1,414,788 distinct signatures were obtained from the MPSS and SBS libraries, respectively. Clustering analysis identified many up- and down-regulated genes specifically and commonly expressed in the cold, drought and salt-treated plant leaves and roots. Data mining revealed the expression patterns of key functional and regulatory genes that were involved in different abiotic stress signaling pathways. Highly conserved cis-regulatory elements in the promoter of the up-regulated genes were identified. Our comprehensive and deep survey of abiotic stress transcriptome of rice has provided candidate genes for further understanding the molecular basis of abiotic stress tolerance in rice.

Current methods for targeted massively parallel sequencing (MPS) have several drawbacks, including limited design flexibility, expense, and protocol complexity, which restrict their application to settings involving modest target size and requiring low cost and high throughput. To address this, we have developed Hi-Plex, a PCR-MPS strategy intended for high-throughput screening of multiple genomic target regions that integrates simple, automated primer design software to control product size. Featuring permissive thermocycling conditions and clamp bias reduction, our protocol is simple, cost- and time-effective, uses readily available reagents, does not require expensive instrumentation, and requires minimal optimization. In a 60-plex assay targeting the breast cancer predisposition genes PALB2 and XRCC2, we applied Hi-Plex to 100 ng LCL-derived DNA, and 100 ng and 25 ng FFPE tumor-derived DNA. Altogether, at least 86.94% of the human genome-mapped reads were on target, and 100% of targeted amplicons were represented within 25-fold of the mean. Using 25 ng FFPE-derived DNA, 95.14% of mapped reads were on-target and relative representation ranged from 10.1-fold lower to 5.8-fold higher than the mean. These results were obtained using only the initial automatically-designed primers present in equal concentration. Hi-Plex represents a powerful new approach for screening panels of genomic target regions.

The development of next-generation sequencing (NGS) technologies has provided novel tools for genome analysis and expression profiling. A high throughput cDNA sequencing method using a bench top next-generation sequencing system, GS Junior, is now available. Here, we used an alternative protocol to the standard method for generating the cDNA library. This protocol can decrease the number of processing steps to manipulate RNA when constructing a cDNA library from an RNA sample, and does not require mRNA isolation from total RNA. Thus it can decrease the risk of RNA degradation and the cost for preparing a cDNA library. Also, the efficiency of sequencing data obtained with this approach is comparable to the standard method as verified by sequencing characteristics and expression levels of the reference gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH).

MicroRNA (miRNA) are highly conserved, noncoding small RNA involved in post-transcriptional gene regulation in a variety of biological processes. To elucidate roles of miRNA in bovine muscle type specification and maintenance, we sought to determine differentially expressed miRNA between semitendinosus (STD) and masseter (MS) muscles from 3 Japanese black cattle by massively parallel sequencing. Differential gene expression of myosin heavy chain (MyHC) isoforms confirmed that STD and MS were MyHC-2x- and MyHC-1-abundant muscles, respectively. In total, 192 known miRNA and 20 potential new bovine miRNA were obtained from the sequencing. The differentially expressed miRNA with more than 2-fold difference in each muscle were identified. In particular, miR-196a and miR-885 were exclusively expressed in STD muscle, which was validated by quantitative reverse transcription-PCR (P=0.045 and P

Understanding how populations of human T cells leverage cellular heterogeneity, plasticity, and diversity to achieve a wide range of functional flexibility, particularly during dynamic processes such as development, differentiation, and antigenic response, is a core challenge that is well suited for single-cell analysis. Hypothesis-free evaluation of cellular states and subpopulations by transcriptional profiling of single T cells can identify relationships that may be obscured by targeted approaches such as FACS sorting on cell-surface antigens, or bulk expression analysis. While this approach is relevant to all cell types, it is of particular interest in the study of T cells for which classical phenotypic criteria are now viewed as insufficient for distinguishing different T cell subtypes and transitional states, and defining the changes associated with dysfunctional T cell states in autoimmunity and tumor-related exhaustion. This unit describes a protocol to generate single-cell transcriptomic libraries of human blood CD4+ and CD8+ T cells, and also introduces the basic bioinformatic steps to process the resulting sequence data for further computational analysis. We show how cellular subpopulations can be identified from transcriptional data, and derive characteristic gene expression signatures that distinguish these states. We believe single-cell RNA-seq is a powerful technique to study the cellular heterogeneity in complex tissues, a paradigm that will be of great value for the immune system.

Objective: Copy number variations (CNVs), a major cause of genetic hearing loss, most frequently involve the STRC gene, located on chr15q15.3 and causally related to autosomal recessive non-syndromic hearing loss (ARNSHL) at the DFNB16 locus. The interpretation of STRC sequence data can be challenging due to the existence of a virtually identical pseudogene, pSTRC, that promotes complex genomic rearrangements in this genomic region. Targeted genomic enrichment with massively parallel sequencing (TGE+MPS) has emerged as the preferred method by which to provide comprehensive genetic testing for hearing loss. We aimed to identify CNVs in the STRC region using established and validated bioinformatics methods. Methods: We used TGE+MPS to identify the genetic cause of hearing loss. The CNV results were confirmed with customized array comparative genomic hybridization (array CGH). Results: Three probands with progressive mild to moderate hearing loss were found among 40 subjects with ARNSHL to segregate homozygous STRC deletions and gene to pseudogene conversion. Array CGH showed that the deletions/conversions span multiple genes outside of the exons captured by TGE+MPS. Conclusion: These data further validate the necessity to integrate the detection of both simple variant changes and complex genomic rearrangements in the clinical diagnosis of genetic hearing loss.

Technological advances in the last decade have resulted in a dramatic increase in the volume of genomic and transcriptomic sequence data available for Vitis vinifera L. and related Vitis species. Until recently, the vast majority of transcript data has been obtained using traditional Sanger sequencing with over 350,000 expressed sequence tags (ESTs) being generated from a limited number of varieties and species. Such EST data has been instrumental in the fabrication of “closed” microarray platforms for assessing mRNA abundance patterns in a wide variety of grapevine tissues and developmental states. Traditional Sanger EST sequencing and massively parallel signature sequencing (MPSS) have now been largely displaced by the emergence of high-throughput “next generation” or “second generation” sequencing technologies that provide “open” deep-sequencing platforms for the characterization of transcriptomes also known as “RNA-Seq”. In this chapter, we review research progress obtained using traditional EST and MPSS as well as summarize more recent RNA-Seq studies that are revolutionizing our understanding of the Vitis transcriptome. We also summarize so-called “third generation” sequencing technologies that promise to improve the effi ciency and lower the cost of genome and transcriptome sequencing in the near future.

With the rapid decline in cost of sequencing, it is now affordable to examine multiple genes in a single disease-targeted clinical test using next generation sequencing. Current targeted sequencing methods require a separate step of targeted capture enrichment during sample preparation before sequencing. Although there are fast sample preparation methods available in market, the library preparation process is still relatively complicated for physicians to use routinely. Here, we introduced an amplification-free Single Molecule Targeted Sequencing (SMTS) technology, which combined targeted capture and sequencing in one step. We demonstrated that this technology can detect low-frequency mutations using artificially synthesized DNA sample. SMTS has several potential advantages, including simple sample preparation thus no biases and errors are introduced by PCR reaction. SMTS has the potential to be an easy and quick sequencing technology for clinical diagnosis such as cancer gene mutation detection, infectious disease detection, inherited condition screening and noninvasive prenatal diagnosis.

Tree breeding is thought to have a pragmatic future. In the backdrop of climate change and exponential increase in population, developing plants, especially trees, resilient to unpredictable environmental conditions is more challenging than ever before. The benefits of tree species, especially fruit trees are multifold for the sustenance of humankind. Tree genotypes are selected and breeding values estimated for superior characteristics following long-term and costly field-based progeny trials, where provenance trials, clone trials and full-sib progeny evaluations are the backbone of such experiments. Of late, efforts are on to manipulate functions at the level of gene and genome of tree species. The shift from traditional Sanger sequencing to massive parallel sequencing of millions of short DNA pieces simultaneously through Next Generation Sequencing (NGS) is a major breakthrough in the last decade. Among the DNA markers, microsatellites (SSRs – Simple Sequence Repeats of 2–6 bases), which tend to be highly polymorphic, locus specific and co-dominant are preferred in Marker Assisted Selection (MAS) and genetic linkage map construction. Reverse genetics, gene silencing through RNA interference (RNAi), Transcript profiling (DNA microarrays), Serial Analysis of Gene Expression (SAGE) and Massively Parallel Signature Sequencing (MPSS) are some of the latest techniques to learn more about functional genomics. The future of tree genomics is bright, but only serious investigations can help to accelerate the systems breeding that integrates information on gene function ensuring superior genotypes.

Differential gene expression analysis has been conventionally performed by microarray techniques; however with the recent advent of next-generation sequencing (NGS) approaches, it has become easier to analyze the coding as well as the noncoding components. Additionally, NGS data analysis also provides information regarding the expression changes of specific isoforms. There are several bioinformatics tools available to analyze NGS data but with different parameters. This chapter provides a comparative insight into these tools by utilizing NGS datasets available from Wt1 knockout and embryonic stem cell line model.