Tool For Analysis Of Gene Expression Research Articles

Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data

BackgroundThe analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the detection of differentially expressed (DE) genes. scRNAseq data, however, are highly heterogeneous and have a large number of zero counts, which introduces challenges in detecting DE genes. Addressing these challenges requires employing new approaches beyond the conventional ones, which are based on a nonzero difference in average expression. Several methods have been developed for differential gene expression analysis of scRNAseq data. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to evaluate and compare the performance of differential gene expression analysis methods for scRNAseq data.ResultsIn this study, we conducted a comprehensive evaluation of the performance of eleven differential gene expression analysis software tools, which are designed for scRNAseq data or can be applied to them. We used simulated and real data to evaluate the accuracy and precision of detection. Using simulated data, we investigated the effect of sample size on the detection accuracy of the tools. Using real data, we examined the agreement among the tools in identifying DE genes, the run time of the tools, and the biological relevance of the detected DE genes.ConclusionsIn general, agreement among the tools in calling DE genes is not high. There is a trade-off between true-positive rates and the precision of calling DE genes. Methods with higher true positive rates tend to show low precision due to their introducing false positives, whereas methods with high precision show low true positive rates due to identifying few DE genes. We observed that current methods designed for scRNAseq data do not tend to show better performance compared to methods designed for bulk RNAseq data. Data multimodality and abundance of zero read counts are the main characteristics of scRNAseq data, which play important roles in the performance of differential gene expression analysis methods and need to be considered in terms of the development of new methods.

(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices.

Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.

Validation of Housekeeping Genes for Gene Expression Profiling in Fish: A Necessity.

Real-time polymerase chain reaction (qPCR) is a reliable tool for gene expression analysis in various organisms. Its use in aquaculture and study of fish physiology is increasing day by day. Relative quantification of mRNA through real-time qPCR requires suitable housekeeping genes/reference genes. Expression of housekeeping genes were thought to be constant in different tissues under various physiological and experimental conditions. In light of the literature reviewed here, it is clear that expression of most of the genes varied in different physiological conditions, under different experimental conditions, and in different gender. Use of housekeeping genes without proper validation may lead to erroneous results and misinterpretation of data. To solve this problem, various types of user-friendly software are now available for validation of housekeeping genes. Moreover, it is recommended to use more than one housekeeping gene for relative quantification. Using the geometric mean of the three most stable genes as a housekeeping gene can also solve this problem to some extent. Therefore, there is a dire need to validate a set of housekeeping genes in the species under study and in various tissues depending on the experimental design to obtain valid and reproducible results.

Hotspot Mutations in SF3B1 Result in Increased Alternative Splicing in Multiple Myeloma and Activation of Key Cellular Pathways

Introduction: Mutations in the components of the spliceosome have been shown to occur at relatively high frequency in many cancers such as chronic lymphocytic leukemia, myelodysplastic syndromes and breast cancer. One component in particular, encoded by SF3B1, has hotspot missense mutations that result in a significant increase in alternatively spliced transcripts. RNA splicing in Multiple Myeloma (MM) has not been investigated and in particular the extent of mutations in SF3B1 and its effects on the transcriptome.Methods: Using the MMRF CoMMpass dataset (N=1273) of newly diagnosed MM patients, samples with whole exome sequencing (WES) were analyzed for mutations using Strelka and Mutect, and samples with SF3B1 mutations identified. A range of approaches were used to explore the effect of the SF3B1 mutations on the transcriptome and to determine possible downstream effects. Using RNA-seq with matched WES samples (n=615), the splice junction usage of SF3B1 mutants was compared against non-mutated samples which were matched for key MM molecular sub-types. The RNA-seq data was analyzed using a pipeline that included STAR and Salmon, aligning to human reference genome hg38, gene and transcript differential expression analysis tools DESeq2 and StringTie/Ballgown, differential splicing exon usage tools JunctionSeq/QoRTs, DEXSeq, and SUPPA and for Gene Set Enrichment Analysis (GSEA) the R package FGSEA was used.Results: From the WES data 1.7% (22/1273) of samples had mutations in SF3B1 of which 5 had mutations in the hotpot codons of K666 and K700. Differential isoform analysis of the 22 SF3B1 mutant samples compared to non-mutated samples did not identify any transcripts. However, when the analysis was restricted to the 5 samples with hotspot mutations differential gene expression identified 146 genes that were significantly differentially expressed at an adjusted p-value <0.05. Additionally, many genes that did not show an overall gene expression change between the control and the SF3B1 hotspot mutants did at transcriptional level where we observed isoform switching which included the protein coding genes BCL2L1, SNUR, ACKR3 and CRLF2.Results of differential gene analysis between the control and SF3B1 mutants were used in GSEA and significant normalized enrichment scores (NES) identifying increased protein secretion (p-value =0.009, NES= 1.9) and unfolded protein response (UPR) (p-value = 0.02, NES = 1.52) pathways. Conversely GSEA identified decreased apoptosis (p-value = 0.008, NES = -1.76), KRAS signaling (p-value = 0.008, NES = -1.92), TNFA signaling via NF-κB (p-value = 0.008, NES= 2.12) pathways in SF3B1 mutant samples.Investigation of splicing loci revealed that novel splice loci were significantly more abundant in the SF3B1 mutants versus control samples. Differential splicing analysis detected 474 genes to be significantly differentially spliced and of those 311 were not found to be differentially expressed at the gene level, indicating that alternative splicing is as important alternative mechanism to gene expression differences. 59 novel splice sites were identified, as well as 152 known splice sites and 218 exon significant differential usage with a p-value of < 0.05. The genes with most significant levels of alternative splicing and found by more than one approach were DYNLL1, TMEM14C, CRNDE, BRD4 and BCL2L1, several of which are also seen in other cancers with mutated SF3B1.Conclusions: Hotspot mutations in SF3B1 result in alternative splicing of genes as well as the introduction of novel splice sites. The confirmation that SF3B1 hotspot mutations in MM increases alternative splicing as well as the identification of the genes undergoing alternative splicing may present novel therapeutic targets. Gene expression analysis of these samples identifies key deregulated pathways, perhaps in response to alternative splicing, including the UPR and protein secretion pathways. These analyses indicate that disruption of these pathways are potential avenues of therapeutic intervention in patients with SF3B1 mutations. DisclosuresOrtiz:Celgene Corporation: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Thakurta:Celgene Corporation: Employment, Equity Ownership. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria.

Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data

BackgroundLong non-coding RNAs (lncRNAs) are typically expressed at low levels and are inherently highly variable. This is a fundamental challenge for differential expression (DE) analysis. In this study, the performance of 25 pipelines for testing DE in RNA-seq data is comprehensively evaluated, with a particular focus on lncRNAs and low-abundance mRNAs. Fifteen performance metrics are used to evaluate DE tools and normalization methods using simulations and analyses of six diverse RNA-seq datasets.ResultsGene expression data are simulated using non-parametric procedures in such a way that realistic levels of expression and variability are preserved in the simulated data. Throughout the assessment, results for mRNA and lncRNA were tracked separately. All the pipelines exhibit inferior performance for lncRNAs compared to mRNAs across all simulated scenarios and benchmark RNA-seq datasets. The substandard performance of DE tools for lncRNAs applies also to low-abundance mRNAs. No single tool uniformly outperformed the others. Variability, number of samples, and fraction of DE genes markedly influenced DE tool performance.ConclusionsOverall, linear modeling with empirical Bayes moderation (limma) and a non-parametric approach (SAMSeq) showed good control of the false discovery rate and reasonable sensitivity. Of note, for achieving a sensitivity of at least 50%, more than 80 samples are required when studying expression levels in realistic settings such as in clinical cancer research. About half of the methods showed a substantial excess of false discoveries, making these methods unreliable for DE analysis and jeopardizing reproducible science. The detailed results of our study can be consulted through a user-friendly web application, giving guidance on selection of the optimal DE tool (http://statapps.ugent.be/tools/AppDGE/).

Abstract 429: Optimization of an RNA sequencing method for low-quantity degraded samples

Abstract RNA-sequencing (RNA-seq) is an effective tool for gene expression analysis, promising to be a diagnostic tool for patient stratification and individualized therapy. However, it has been challenging to apply RNA-seq to low-quantity and degraded RNA derived from formalin fixed paraffin embedded clinical samples. The Illumina TruSeq® RNA Access approach, utilizing capture probes targeting known exons to enrich for coding RNAs, has shown high performance to profile poor quality RNA samples at input amounts at or above 20 ng. Here, we have further optimized the Illumina TruSeq® RNA Access method for samples with low quality and low quantity, and have compared results to those obtained with libraries prepared with SMARTer® Stranded Total RNA-Seq- Pico Input Kit. Libraries were prepared using 1 ng and 10 ng of degraded RNA from Banked FFPE liver specimens, and were compared to libraries prepared using the standard RNA Access method with an input of 100 ng. With the optimized low-input RNA Access method, the alignment rate was consistently high (95-97%) across both input amounts, with 82-84% of aligned reads mapped to the transcriptome. This is comparable to results obtained with the standard 100 ng input. Gene detection however, changed as a function of input, with ~ 15,900 genes detected at an input of 10 ng, and ~ 13,500 detected at 1 ng of input. In contrast, the SMARTer method detected a relatively constant number of genes (~ 14,500 genes) at both input amounts, albeit with a lower % aligned reads to transcriptome (46-47%), and an increased % reads aligned to Ribosomal RNA when compared to RNA Access. Thus, while both methods are capable of comparable levels of performance, the RNA Access method is more cost-effective from a sequencing standpoint. These characteristics were maintained across a group of additional FFPE-derived samples tested by both methods. Taken together, these results demonstrate that an optimized RNA Access method has consistent mapping performance and a high gene detection rate, and is thus suitable for the analysis of low-quantity degraded RNA. Citation Format: Dan Su, Jason G. Powers, Thomas A. Halsey, Patrick Hurban. Optimization of an RNA sequencing method for low-quantity degraded samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 429.

Reference gene selection for RT-qPCR analysis in Harmonia axyridis, a global invasive lady beetle

Harmonia axyridis is a voracious predator, a biological control agent, and one of the world most invasive insect species. The advent of next-generation sequencing platforms has propelled entomological research into the genomics and post-genomics era. Real-time quantitative PCR (RT-qPCR), a primary tool for gene expression analysis, is a core technique governs the genomic research. The selection of internal reference genes, however, can significantly impact the interpretation of RT-qPCR results. The overall goal of this study is to identify the reference genes in the highly invasive H. axyridis. Our central hypothesis is that the suitable reference genes for RT-qPCR analysis can be selected from housekeeping genes. To test this hypothesis, the stability of nine housekeeping genes, including 18S, 28S, ACTB, ATP1A1, GAPDH, HSP70, HSP90, RP49, and ATP6V1A, were investigated under both biotic (developmental time, tissue and sex), and abiotic (temperature, photoperiod, in vivo RNAi) conditions. Gene expression profiles were analyzed by geNorm, Normfinder, BestKeeper, and the ΔCt method. Our combined results recommend a specific set of reference genes for each experimental condition. With the recent influx of genomic information for H. axyridis, this study lays the foundation for an in-depth omics dissection of biological invasion in this emerging model.

KM-express: an integrated online patient survival and gene expression analysis tool for the identification and functional characterization of prognostic markers in breast and prostate cancers.

The identification and functional characterization of novel biomarkers in cancer requires survival analysis and gene expression analysis of both patient samples and cell line models. To help facilitate this process, we have developed KM-Express. KM-Express holds an extensive manually curated transcriptomic profile of 45 different datasets for prostate and breast cancer with phenotype and pathoclinical information, spanning from clinical samples to cell lines. KM-Express also contains The Cancer Genome Atlas datasets for 30 other cancer types with matching cell line expression data for 23 of them. We present KM-Express as a hypothesis generation tool for researchers to identify potential new prognostic RNA biomarkers as well as targets for further downstream functional cell-based studies. Specifically, KM-Express allows users to compare the expression level of genes in different groups of patients based on molecular, genetic, clinical and pathological status. Moreover, KM-Express aids the design of biological experiments based on the expression profile of the genes in different cell lines. Thus, KM-Express provides a one-stop analysis from bench work to clinical prospects. We have used this tool to successfully evaluate the prognostic potential of previously published biomarkers for prostate cancer and breast cancer. We believe KM-Express will accelerate the translation of biomedical research from bench to bed.Database URL: http://ec2-52-201-246-161.compute-1.amazonaws.com/kmexpress/index.php

Valid gene expression normalization by RT-qPCR in studies on hPDL fibroblasts with focus on orthodontic tooth movement and periodontitis

Meaningful, reliable and valid mRNA expression analyses by real-time quantitative PCR (RT-qPCR) can only be achieved, if suitable reference genes are chosen for normalization and if appropriate RT-qPCR quality standards are met. Human periodontal ligament (hPDL) fibroblasts play a major mediating role in orthodontic tooth movement and periodontitis. Despite corresponding in-vitro gene expression studies being a focus of interest for many years, no information is available for hPDL fibroblasts on suitable reference genes, which are generally used in RT-qPCR experiments to normalize variability between samples. The aim of this study was to identify and validate suitable reference genes for normalization in untreated hPDL fibroblasts as well as experiments on orthodontic tooth movement or periodontitis (Aggregatibacter actinomycetemcomitans). We investigated the suitability of 13 candidate reference genes using four different algorithms (geNorm, NormFinder, comparative ΔCq and BestKeeper) and ranked them according to their expression stability. Overall PPIB (peptidylprolyl isomerase A), TBP (TATA-box-binding protein) and RPL22 (ribosomal protein 22) were found to be most stably expressed with two genes in conjunction sufficient for reliable normalization. This study provides an accurate tool for quantitative gene expression analysis in hPDL fibroblasts according to the MIQE guidelines and shows that reference gene reliability is treatment-specific.

Microarray as High Throughput Tool for Tyrosinase Gene Expression Analysis

Hyper pigmentary problems including post inflammatory hyper pigmentation solar lentigos and melasma occur widely in the human population and are thus of broad interest for control Tyrosinase is the key enzyme of melanogenesis and it rsquo s over expression results in hyper pigmentation Tyrosinase is not only liable to human pigmentation but also responsible for the undesirable enzymatic browning of fruits and vegetables which is of economic importance So in order to get rid of the problem inhibitors of the enzyme tyrosinase have been concerned Advances in genomics and proteomics have opened up new possibilities for the rapid functional assignment and global characterization of nbsp proteins and DNA Organized collection of molecules on arrays have provided a vigorous platform for rapid screening lead discovery and eradication of molecular characterization Nowadays it is used for the discovery of tyrosinase inhibitor It involves microarrays of immobilized nbsp distinct small molecules or DNA or RNA of cells treated with molecules to be tested and binding of tyrosinase proteins or TYR gene respectively Compound is then screened and the respective compound is identified as a new small molecule binder to nbsp tyrosinase and used it as potential tyrosinase inhibitor Its actual binding and inhibitory effects on nbsp tyrosinase nbsp is also validated using an enzyme based inhibition assay or other binding assays As a rapidly maturing technology high throughput proteomic and genomic pave the way forward in the treatment of skin hyper pigmentation Hence in the present review we have focused on the use of microarray technique over the conventional strategies for the elucidation of tyrosinase inhibitors

A comprehensive assessment of RNA-seq protocols for degraded and low-quantity samples

BackgroundRNA-sequencing (RNA-seq) has emerged as one of the most sensitive tool for gene expression analysis. Among the library preparation methods available, the standard poly(A) + enrichment provides a comprehensive, detailed, and accurate view of polyadenylated RNAs. However, on samples of suboptimal quality ribosomal RNA depletion and exon capture methods have recently been reported as better alternatives.MethodsWe compared for the first time three commercial Illumina library preparation kits (TruSeq Stranded mRNA, TruSeq Ribo-Zero rRNA Removal, and TruSeq RNA Access) as representatives of these three different approaches using well-established human reference RNA samples from the MAQC/SEQC consortium on a wide range of input amounts (from 100 ng down to 1 ng) and degradation levels (intact, degraded, and highly degraded).ResultsWe assessed the accuracy of the generated expression values by comparison to gold standard TaqMan qPCR measurements and gained unprecedented insight into the limits of applicability in terms of input quantity and sample quality of each protocol. We found that each protocol generates highly reproducible results (R2 > 0.92) on intact RNA samples down to input amounts of 10 ng. For degraded RNA samples, Ribo-Zero showed clear performance advantages over the other two protocols as it generated more accurate and better reproducible gene expression results even at very low input amounts such as 1 ng and 2 ng. For highly degraded RNA samples, RNA Access performed best generating reliable data down to 5 ng input.ConclusionsWe found that the ribosomal RNA depletion protocol from Illumina works very well at amounts far below recommendation and over a good range of intact and degraded material. We also infer that the exome-capture protocol (RNA Access, Illumina) performs better than other methods on highly degraded and low amount samples.

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project.

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged as a powerful tool for gene-expression analysis and transcriptome mapping. However, handling RNA-Seq datasets requires sophisticated computational expertise and poses inherent challenges for biology researchers. This bottleneck has been mitigated by the open access Galaxy project that allows users without bioinformatics skills to analyze RNA-Seq data, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID), a Gene Ontology (GO) term analysis suite that helps derive biological meaning from large data sets. However, for first-time users and bioinformatics' amateurs, self-learning and familiarization with these platforms can be time-consuming and daunting. We describe a straightforward workflow that will help C. elegans researchers to isolate worm RNA, conduct an RNA-Seq experiment and analyze the data using Galaxy and DAVID platforms. This protocol provides stepwise instructions for using the various Galaxy modules for accessing raw NGS data, quality-control checks, alignment, and differential gene expression analysis, guiding the user with parameters at every step to generate a gene list that can be screened for enrichment of gene classes or biological processes using DAVID. Overall, we anticipate that this article will provide information to C. elegans researchers undertaking RNA-Seq experiments for the first time as well as frequent users running a small number of samples.

Identifying Significantly Impacted Pathways and Putative Mechanisms with iPathwayGuide.

iPathwayGuide is a gene expression analysis tool that provides biological context and inferences from data generated by high-throughput sequencing. iPathwayGuide utilizes a systems biology approach to identify significantly impacted signaling pathways, Gene Ontology terms, disease processes, predicted microRNAs, and putative mechanisms based on the given differential expression signature. By using a novel analytical approach called Impact Analysis, iPathwayGuide considers the role, position, and relationships of each gene within a pathway, which results in a significant reduction in false positives, as well as a better ability to identify the truly impacted pathways and putative mechanisms that can explain all measured gene expression changes. It is a Web-based, user-friendly, interactive tool that does not require prior training in bioinformatics. The protocols in this unit describe how to use iPathwayGuide to analyze a single contrast between two phenotypes (any number of samples), and provide guidance on how to interpret the results obtained from iPathwayGuide. Even though iPathwayGuide has powerful meta-analysis capabilities, these are not covered in this unit. © 2017 by John Wiley & Sons, Inc.

Mature Luffa Leaves (Luffa cylindrica L.) as a Tool for Gene Expression Analysis by Agroinfiltration.

We exploited the potential of cucurbits for ectopic gene expression. Agroinfiltration is a simple and commonly used method to obtain transient expression of foreign genes in plants. In contrast to in vitro transformation techniques, agroinfiltration can be used for genetic modification of mature plant tissues. Although the cucurbits are commonly used as model plants for molecular biology and biotechnology studies, to date there are no literature sources on the possibility of transient gene expression in mature cucurbit tissues. Our research has shown that mature leaves of Luffa cylindrica L. (luffa), in contrast to other cucurbit species, can be successfully transiently transformed with Agrobacterium tumefaciens. We efficiently transformed luffa leaves with a reporter gene encoding β-glucuronidase (GUS). The GUS activity in transiently transformed leaf tissues was detected within 24 h after the infiltration with bacteria. Additionally, we have shown that the activity of a transiently expressed the GUS gene can be monitored directly in the EDTA-exudates collected from the cut petioles of the agroinfiltrated leaves. The results suggest that luffa leaves can be useful as a plant expression system for studies of physiological and biochemical processes in cucurbits.

Identification and evaluation of reference genes for accurate gene expression normalization of fresh and frozen-thawed spermatozoa of water buffalo (Bubalus bubalis)

The quantitative real time PCR (qRT-PCR) has become an important tool for gene-expression analysis for a selected number of genes in life science. Although large dynamic range, sensitivity and reproducibility of qRT-PCR is good, the reliability majorly depend on the selection of proper reference genes (RGs) employed for normalization. Although, RGs expression has been reported to vary considerably within same cell type with different experimental treatments. No systematic study has been conducted to identify and evaluate the appropriate RGs in spermatozoa of domestic animals. Therefore, this study was conducted to analyze suitable stable RGs in fresh and frozen-thawed spermatozoa. We have assessed 13 candidate RGs (BACT, RPS18s, RPS15A, ATP5F1, HMBS, ATP2B4, RPL13, EEF2, TBP, EIF2B2, MDH1, B2M and GLUT5) of different functions and pathways using five algorithms. Regardless of the approach, the ranking of the most and the least candidate RGs remained almost same. The comprehensive ranking by RefFinder showed GLUT5, ATP2B4 and B2M, MDH1 as the top two stable and least stable RGs, respectively. The expression levels of four heat shock proteins (HSP) were employed as a target gene to evaluate RGs efficiency for normalization. The results demonstrated an exponential difference in expression levels of the four HSP genes upon normalization of the data with the most stable and the least stable RGs. Our study, provides a convenient RGs for normalization of gene-expression of key metabolic pathways effected during freezing and thawing of spermatozoa of buffalo and other closely related bovines.

Construction of an Ostrea edulis database from genomic and expressed sequence tags (ESTs) obtained from Bonamia ostreae infected haemocytes: Development of an immune-enriched oligo-microarray

The flat oyster, Ostrea edulis, is one of the main farmed oysters, not only in Europe but also in the United States and Canada. Bonamiosis due to the parasite Bonamia ostreae has been associated with high mortality episodes in this species. This parasite is an intracellular protozoan that infects haemocytes, the main cells involved in oyster defence. Due to the economical and ecological importance of flat oyster, genomic data are badly needed for genetic improvement of the species, but they are still very scarce. The objective of this study is to develop a sequence database, OedulisDB, with new genomic and transcriptomic resources, providing new data and convenient tools to improve our knowledge of the oyster's immune mechanisms. Transcriptomic and genomic sequences were obtained using 454 pyrosequencing and compiled into an O. edulis database, OedulisDB, consisting of two sets of 10,318 and 7159 unique sequences that represent the oyster's genome (WG) and de novo haemocyte transcriptome (HT), respectively. The flat oyster transcriptome was obtained from two strains (naïve and tolerant) challenged with B. ostreae, and from their corresponding non-challenged controls. Approximately 78.5% of 5619 HT unique sequences were successfully annotated by Blast search using public databases. A total of 984 sequences were identified as being related to immune response and several key immune genes were identified for the first time in flat oyster. Additionally, transcriptome information was used to design and validate the first oligo-microarray in flat oyster enriched with immune sequences from haemocytes. Our transcriptomic and genomic sequencing and subsequent annotation have largely increased the scarce resources available for this economically important species and have enabled us to develop an OedulisDB database and accompanying tools for gene expression analysis. This study represents the first attempt to characterize in depth the O. edulis haemocyte transcriptome in response to B. ostreae through massively sequencing and has aided to improve our knowledge of the immune mechanisms of flat oyster. The validated oligo-microarray and the establishment of a reference transcriptome will be useful for large-scale gene expression studies in this species.

Gene-set activity toolbox (GAT): A platform for microarray-based cancer diagnosis using an integrative gene-set analysis approach.

Cancer is a complex disease that cannot be diagnosed reliably using only single gene expression analysis. Using gene-set analysis on high throughput gene expression profiling controlled by various environmental factors is a commonly adopted technique used by the cancer research community. This work develops a comprehensive gene expression analysis tool (gene-set activity toolbox: (GAT)) that is implemented with data retriever, traditional data pre-processing, several gene-set analysis methods, network visualization and data mining tools. The gene-set analysis methods are used to identify subsets of phenotype-relevant genes that will be used to build a classification model. To evaluate GAT performance, we performed a cross-dataset validation study on three common cancers namely colorectal, breast and lung cancers. The results show that GAT can be used to build a reasonable disease diagnostic model and the predicted markers have biological relevance. GAT can be accessed from http://gat.sit.kmutt.ac.th where GAT's java library for gene-set analysis, simple classification and a database with three cancer benchmark datasets can be downloaded.

Impaired Expression of Cytokines as a Result of Viral Infections with an Emphasis on Small Ruminant Lentivirus Infection in Goats.

Knowing about the genes involved in immunity, and being able to identify the factors influencing their expressions, helps in gaining awareness of the immune processes. The qPCR method is a useful gene expression analysis tool, but studies on immune system genes are still limited, especially on the caprine immune system. Caprine arthritis encephalitis, a disease caused by small ruminant lentivirus (SRLV), causes economic losses in goat breeding, and there is no therapy against SRLV. The results of studies on vaccines against other viruses are promising. Moreover, the Marker-Assisted Selection strategy against SRLV is possible, as has been shown in sheep breeding. However, there are still many gaps in our knowledge on the caprine immune response to infection. All types of cytokines play pivotal roles in immunity, and SRLV infection influences the expression of many cytokines in different types of cells. This information encouraged the authors to examine the results of studies conducted on SRLV and other viral infections, with an emphasis on the expression of cytokine genes. This review attempts to summarize the results of studies on the expression of cytokines in the context of the SRLV infection.

Protoplast isolation, transient transformation of leaf mesophyll protoplasts and improved Agrobacterium-mediated leaf disc infiltration of Phaseolus vulgaris: tools for rapid gene expression analysis.

BackgroundPhaseolus vulgaris is one of the most extensively studied model legumes in the world. The P. vulgaris genome sequence is available; therefore, the need for an efficient and rapid transformation system is more imperative than ever. The functional characterization of P. vulgaris genes is impeded chiefly due to the non-amenable nature of Phaseolus sp. to stable genetic transformation. Transient transformation systems are convenient and versatile alternatives for rapid gene functional characterization studies. Hence, the present work focuses on standardizing methodologies for protoplast isolation from multiple tissues and transient transformation protocols for rapid gene expression analysis in the recalcitrant grain legume P. vulgaris.ResultsHerein, we provide methodologies for the high-throughput isolation of leaf mesophyll-, flower petal-, hypocotyl-, root- and nodule-derived protoplasts from P. vulgaris. The highly efficient polyethylene glycol-mannitol magnesium (PEG-MMG)-mediated transformation of leaf mesophyll protoplasts was optimized using a GUS reporter gene. We used the P. vulgaris SNF1-related protein kinase 1 (PvSnRK1) gene as proof of concept to demonstrate rapid gene functional analysis. An RT-qPCR analysis of protoplasts that had been transformed with PvSnRK1-RNAi and PvSnRK1-OE vectors showed the significant downregulation and ectopic constitutive expression (overexpression), respectively, of the PvSnRK1 transcript. We also demonstrated an improved transient transformation approach, sonication-assisted Agrobacterium-mediated transformation (SAAT), for the leaf disc infiltration of P. vulgaris. Interestingly, this method resulted in a 90 % transformation efficiency and transformed 60–85 % of the cells in a given area of the leaf surface. The constitutive expression of YFP further confirmed the amenability of the system to gene functional characterization studies.ConclusionsWe present simple and efficient methodologies for protoplast isolation from multiple P. vulgaris tissues. We also provide a high-efficiency and amenable method for leaf mesophyll transformation for rapid gene functional characterization studies. Furthermore, a modified SAAT leaf disc infiltration approach aids in validating genes and their functions. Together, these methods help to rapidly unravel novel gene functions and are promising tools for P. vulgaris research.Electronic supplementary materialThe online version of this article (doi:10.1186/s12896-016-0283-8) contains supplementary material, which is available to authorized users.

Discrete distributional differential expression (D3E)--a tool for gene expression analysis of single-cell RNA-seq data.

BackgroundThe advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions.ResultsWe present a discrete, distributional method for differential gene expression (D3E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D3E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D3E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D3E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.ConclusionEvaluation using synthetic data shows that D3E performs better than other methods for identifying differentially expressed genes since it is designed to take full advantage of the information available from single-cell RNA-seq experiments. Moreover, the analytical model underlying D3E makes it possible to gain additional biological insights.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0944-6) contains supplementary material, which is available to authorized users.