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The development of a safe, effective, reversible, non-hormonal contraceptive method for men has been an ongoing effort for the past few decades. However, despite significant progress on elucidating the function of key proteins involved in reproduction, understanding male reproductive physiology is limited by incomplete information on the genes expressed in reproductive tissues, and no contraceptive targets have so far reached clinical trials. To advance product development, further identification of novel reproductive tract-specific genes leading to potentially druggable protein targets is imperative. In this study, we expand on previous single tissue, single species studies by integrating analysis of publicly available human and mouse RNA-seq datasets whose initial published purpose was not focused on identifying male reproductive tract-specific targets. We also incorporate analysis of additional newly acquired human and mouse testis and epididymis samples to increase the number of targets identified.

Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data. We conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of

The information contained in the genome of an organism, its DNA, is expressed through transcription of its genes to RNA, in quantities determined by many internal and external factors. As such, studying the gene expression can give valuable information for e.g. clinical diagnostics. A common analysis workflow of RNA-sequencing (RNA-seq) data consists of mapping the sequencing reads to a reference genome, followed by the transcript assembly and quantification based on these alignments. The advent of second-generation sequencing revolutionized the field by reducing the sequencing costs by 50,000-fold. Now another revolution is imminent with the third-generation sequencing platforms producing an order of magnitude higher read lengths. However, higher error rate, higher cost and lower throughput compared to the second-generation sequencing bring their own challenges. To compensate for the low throughput and high cost, hybrid approaches using both short second-generation and long third-generation ...

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Abstract] Nowadays, the analysis of transcriptome sequencing (RNA-seq) data has become the standard method for quantifying the levels of gene expression. In RNA-seq experiments, the mapping of short reads to a reference genome or transcriptome is considered a crucial step that remains as one of the most time-consuming. With the steady development of Next Generation Sequencing (NGS) technologies, unprecedented amounts of genomic data introduce significant challenges in terms of storage, processing and downstream analysis. As cost and throughput continue to improve, there is a growing need for new software solutions that minimize the impact of increasing data volume on RNA read alignment. In this work we introduce HSRA, a Big Data tool that takes advantage of the MapReduce programming model to extend the multithreading capabilities of a state-of-the-art spliced read aligner for RNA-seq data (HISAT2) to distributed memory systems such as multi-core clusters or cloud platforms. HSRA has been built ...

The rapid expansion of transcriptomics from increased affordability of next-generation sequencing (NGS) technologies generates rocketing amounts of gene expression data across biology and medicine, and notably in cancer research. Concomitantly, many bioinformatics tools were developed to streamline gene expression analysis and quantification. We tested the concordance of NGS RNA sequencing (RNA-seq) analysis outcomes between the two predominant programs for reads alignment, HISAT2 and STAR, and the two most popular programs for quantifying gene expression in NGS experiments, edgeR and DESeq2, using RNA-seq data from a series of breast cancer progression specimens, which include histologically confirmed normal, early neoplasia, ductal carcinoma in situ and infiltrating ductal carcinoma samples microdissected from formalin fixed, paraffin embedded (FFPE) breast tissue blocks. We identified significant differences in aligners’ performance: HISAT2 was prone to misalign reads to retrogene genomic loci,

Bacterial resistance to antibiotics is a growing health problem that is projected to cause more deaths than cancer by 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the structurally conserved bacterial ribosomes, factors involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. Here, we use a bioinformatics approach to identify novel components of protein synthesis. In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct

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Purpose: Ischemia-reperfusion injury (IRI) during liver transplantation is an important factor that leads to graft dysfunction and even recipient death. This study aims to explore the effect of IRI on the transcriptome of intrahepatic cells in single cell level. *Methods: We collected tissue samples from donor liver before procurement, post-preservation and 2 hours post-reperfusion and then performed single-cell RNA, T cell receptor (TCR) and B cell receptor (BCR) sequencing. We annotated the subpopulations of intrahepatic cells and compared their changes in gene expression profiling after IRI. Combined with the bulk RNA sequencing data in our center, we explored the potential therapeutic targets of IRI. *Results: After enzymatic digestion and fluorescence activated cell sorting (FACS), 15013 cells were captured and sub-clustered into 17 subsets (Fig. 1A). We annotated each cluster and described the abundance and number of differentially expressed genes in each cluster after IRI (Fig. 1B & C). ...

Although RNA sequencing (RNA-seq) has become the most advanced technology for transcriptome analysis, it also confronts various challenges. As we all know, the workflow of RNA-seq is extremely complicated and it is easy to produce bias. This may damage the quality of RNA-seq dataset and lead to an incorrect interpretation for sequencing result. Thus, our ...

TY - JOUR. T1 - Transcript annotation in FANTOM3. T2 - Mouse gene catalog based on physical cDNAs. AU - Maeda, Norihiro. AU - Kasukawa, Takeya. AU - Oyama, Rieko. AU - Gough, Julian. AU - Frith, Martin. AU - Engström, Pär G.. AU - Lenhard, Boris. AU - Aturaliya, Rajith N.. AU - Batalov, Serge. AU - Beisel, Kirk W.. AU - Bult, Carol J.. AU - Fletcher, Colin F.. AU - Forrest, Alistair R.R.. AU - Furuno, Masaaki. AU - Hill, David. AU - Itoh, Masayoshi. AU - Kanamori-Katayama, Mutsumi. AU - Katayama, Shintaro. AU - Katoh, Masaru. AU - Kawashima, Tsugumi. AU - Quackenbushb, John. AU - Ravasi, Timothy. AU - Ring, Brian Z.. AU - Shibata, Kazuhiro. AU - Sugiura, Koji. AU - Takenaka, Yoichi. AU - Teasdale, Rohan D.. AU - Wells, Christine A.. AU - Zhu, Yunxia. AU - Kai, Chikatoshi. AU - Kawai, Jun. AU - Hume, David A.. AU - Carninci, Piero. AU - Hayashizaki, Yoshihide. PY - 2006/4/1. Y1 - 2006/4/1. N2 - The international FANTOM consortium aims to produce a comprehensive picture of the mammalian ...

New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.

The recent development of sensitive protocols allows to generate RNA-seq libraries of single cells. The throughput of such scRNA-seq protocols is rapidly increasing, enabling the profiling of tens of thousands of cells and opening exciting possibilities to analyse cellular identities. In this context, unique molecular identifiers (UMIs) are used to reduce amplification noise and sample-specific ...

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Here, using single-cell RNA sequencing, we examine the stromal compartment in murine melanoma and draining lymph nodes (LNs) at points across tumor development, providing data at http://www.teichlab.org/data/. Naive lymphocytes from LNs undergo activation and clonal expansion within the tumor, befor …

Live Webinar on RNA-Seq Data Analysis Abstract: Strand NGS supports an extensive workflow for the analysis and visualization of RNA-Seq data. The workflow includes Transcriptome / Genome alignment, Differential expression analysis with Statistical ...

Transcriptome analysis using RNA sequencing (RNA-seq) empowers a deeper understanding of genetics by enabling RNA expression analysis over the entire transcriptome with high sensitivity and a wide dynamic range. One powerful application within this field is stranded total RNA-seq, which makes it possible to distinguish overlapping genes and to conduct comprehensive annotation and quantification of long noncoding RNAs. Typical solutions for total RNA-seq library prep require relatively high input amounts, in the range of 100 ng to 1 µg, and it is standard practice to remove the ribosomal RNA (rRNA) from the total RNA sample prior to cDNA synthesis and library preparation. Clontech was a pioneer in the development of a low-input solution, RiboGone technology for rRNA removal from total RNA, enabling library construction from inputs spanning 10 ng to 100 ng. We integrated this technology into our SMARTer stranded RNA-seq kits, reducing the representation of rRNA in the final libraries and leading ...

Transcriptome analysis using RNA sequencing (RNA-seq) empowers a deeper understanding of genetics by enabling RNA expression analysis over the entire transcriptome with high sensitivity and a wide dynamic range. One powerful application within this field is stranded total RNA-seq, which makes it possible to distinguish overlapping genes and to conduct comprehensive annotation and quantification of long noncoding RNAs. Typical solutions for total RNA-seq library prep require relatively high input amounts, in the range of 100 ng to 1 µg, and it is standard practice to remove the ribosomal RNA (rRNA) from the total RNA sample prior to cDNA synthesis and library preparation. Clontech was a pioneer in the development of a low-input solution, RiboGone technology for rRNA removal from total RNA, enabling library construction from inputs spanning 10 ng to 100 ng. We integrated this technology into our SMARTer stranded RNA-seq kits, reducing the representation of rRNA in the final libraries and leading ...

For example, a 3 mRNA-Seq approach generates sequencing reads localized to the 3 end of mRNAs. Therefore, it is a highly convenient method for multiplexing a large number of samples, does not require poly(A) enrichment prior to library preparation, and allows to accurately quantify gene expression with minimal computational resources. You can find short overview of read depth requirements in our blog article How many reads do I need for my RNA-Seq samples?. These features make 3 mRNA-Seq the method of choice for gene expression profiling, especially for high-throughput projects. However, due to the fact that reads are localized to the 3 ends of the transcripts, this method is not suitable to assess alternative splicing within the transcript body, investigate differential transcript usage, or for the identification of transcript isoforms. Thus, if this level of information is required, a whole transcriptome library preparation method that provides full transcript coverage would be the ...

This article will focus on conventional applications of RNA Sequencing, and will explore mining information for cSNP, Insertions Deletions & Fusion Genes, Alternate Splicing, Novel Genes/Exon, eQTL, and more. RNA Sequencing is a treasure-chest of information and quiet often we miss on potential ground breaking information in the RNA-SEQ datasets. While we normally plan an. Read More. ...

The Xenopus embryo has provided key insights into fate specification, the cell cycle, and other fundamental developmental and cellular processes, yet a comprehensive understanding of its transcriptome is lacking. Here, we used paired end RNA sequencing (RNA-seq) to explore the transcriptome of Xenopus tropicalis in 23 distinct developmental stages. We determined expression levels of all genes annotated in RefSeq and Ensembl and showed for the first time on a genome-wide scale that, despite a general state of transcriptional silence in the earliest stages of development, approximately 150 genes are transcribed prior to the midblastula transition. In addition, our splicing analysis uncovered more than 10,000 novel splice junctions at each stage and revealed that many known genes have additional unannotated isoforms. Furthermore, we used Cufflinks to reconstruct transcripts from our RNA-seq data and found that ∼13.5% of the final contigs are derived from novel transcribed regions, both within ...

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This data set contains the raw .fastq files from two RNA-sequencing experiments and two small RNA-sequencing experiments. Both control brain tissue and tissue from sufferers of mesial temporal lobe epilepsy were sequenced. Two different brain regions were sequenced; the cortex and the hippocampus. For more details please see: Mills, James D., et al. Coding and non-coding transcriptome of mesial temporal lobe epilepsy: Critical role of small non-coding RNAs. Neurobiology of disease 134 (2020): 104612 ...

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Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl.. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in humans. Human transcript sequences from the International Nucleotide Sequence Database ...

RNA sequencing reveals the differences between chemical and chronic constipation induction of intestinal tumor, Yunpeng Luan, Yong Cao, Dechang Mao, Yanmei Li, Xiaoguang Yue, Youji

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Manduca sexta is a large lepidopteran insect widely used as a model to study biochemistry of insect physiological processes. As a part of its genome project, over 50 cDNA libraries have been analyzed to profile gene expression in different tissues and life stages. While the RNA-seq data were used to study genes related to cuticle structure, chitin metabolism and immunity, a vast amount of the information has not yet been mined for understanding the basic molecular biology of this model insect. In fact, the basic features of these data, such as composition of the RNA-seq reads and lists of library-correlated genes, are unclear. From an extended view of all insects, clear-cut tempospatial expression data are rarely seen in the largest group of animals including Drosophila and mosquitoes, mainly due to their small sizes. We obtained the transcriptome data, analyzed the raw reads in relation to the assembled genome, and generated heatmaps for clustered genes. Library

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From the Department of Developmental Biology (K.-C.Y., J.M.N.) and Center for Cardiovascular Research, Division of Cardiology, Department of Internal Medicine (K.A.Y., A.Y.P., V.K.T., G.A.E., D.L.M.), Washington University Medical School, St. Louis, MO; Division of Cardiothoracic Surgery, New York Presbyterian Hospital, Columbia University College of Physicians and Surgeons, New York, NY (I.G.); and Department of Surgery, University of Maryland School of Medicine, Baltimore (F.H.C.). Dr Yangs current affiliation is the Department of Pharmacology, National Taiwan University School of Medicine, Taipei, Taiwan. ...

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Massively parallel cDNA sequencing (RNA-seq) experiments are gradually superseding microarrays in quantitative gene expression profiling. However, many biologists are uncertain about the choice of differentially expressed gene (DEG) analysis methods and the validity of cost-saving sample pooling strategies for their RNA-seq experiments. Hence, we performed experimental validation of DEGs identified by Cuffdiff2, edgeR, DESeq2 and Two-stage Poisson Model (TSPM) in a RNA-seq experiment involving mice amygdalae micro-punches, using high-throughput qPCR on independent biological replicate samples. Moreover, we sequenced RNA-pools and compared their results with sequencing corresponding individual RNA samples. False-positivity rate of Cuffdiff2 and false-negativity rates of DESeq2 and TSPM were high. Among the four investigated DEG analysis methods, sensitivity and specificity of edgeR was relatively high. We documented the pooling bias and that the DEGs identified in pooled samples suffered low positive

Covaris Adaptive Focused Acoustics® (AFA®) has long been the gold standard for unbiased, efficient, and highly reproducible shearing of DNA for NGS library preparation workflows. The same technology is also available for an unbiased, efficient, and highly reproducible shearing of total RNA, mRNA, and cDNA for RNA-Seq library preparation.. RNA sequencing applications using NGS have been the technique of choice for carrying out whole transcriptome sequencing, monitoring gene expression, and elucidating products of gene splicing and post transcriptional modifications. The recent implication of gene fusion events in tumorigenesis and their potential utility in diagnostics enables a higher level of tumor classification. In drug discovery, it specifically targets products of gene fusion events. This combination brings new focus on the use of RNA-Seq in the clinic for tumor classification, cancer progression, and treatment monitoring.. Chemical fragmentation of RNA is highly sensitive to sample ...

PubMed Central Canada (PMC Canada) provides free access to a stable and permanent online digital archive of full-text, peer-reviewed health and life sciences research publications. It builds on PubMed Central (PMC), the U.S. National Institutes of Health (NIH) free digital archive of biomedical and life sciences journal literature and is a member of the broader PMC International (PMCI) network of e-repositories.

Replication Data for publication: Enterococcus faecalis adapts to antimicrobial conjugated oligoelectrolytes by lipid rearrangement and differential expression of membrane stress response genes. Includes: RNA sequencing data: RAW reads RNA sequencing data: non rRNA reads RNA sequencing data: extracted counts See RNAseq-readme.txt for file description.

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PubMed Central Canada (PMC Canada) provides free access to a stable and permanent online digital archive of full-text, peer-reviewed health and life sciences research publications. It builds on PubMed Central (PMC), the U.S. National Institutes of Health (NIH) free digital archive of biomedical and life sciences journal literature and is a member of the broader PMC International (PMCI) network of e-repositories.

BACKGROUND: Renal endothelial cells from glomerular, cortical, and medullary kidney compartments are exposed to different microenvironmental conditions and support specific kidney processes. However, the heterogeneous phenotypes of these cells remain incompletely inventoried. Osmotic homeostasis is vitally important for regulating cell volume and function, and in mammals, osmotic equilibrium is regulated through the countercurrent system in the renal medulla, where water exchange through endothelium occurs against an osmotic pressure gradient. Dehydration exposes medullary renal endothelial cells to extreme hyperosmolarity, and how these cells adapt to and survive in this hypertonic milieu is unknown.. METHODS: We inventoried renal endothelial cell heterogeneity by single-cell RNA sequencing ,40,000 mouse renal endothelial cells, and studied transcriptome changes during osmotic adaptation upon water deprivation. We validated our findings by immunostaining and functionally by targeting oxidative ...

TopHat-Fusion is an algorithm designed to discover transcripts representing fusion gene products, which result from the breakage and re-joining of two different chromosomes, or from rearrangements within a chromosome. TopHat-Fusion is an enhanced version of TopHat, an efficient program that aligns RNA-seq reads without relying on existing annotation. Because it is independent of gene annotation, TopHat-Fusion can discover fusion products deriving from known genes, unknown genes and unannotated splice variants of known genes. Using RNA-seq data from breast and prostate cancer cell lines, we detected both previously reported and novel fusions with solid supporting evidence. TopHat-Fusion is available at http://tophat-fusion.sourceforge.net/ .

Bioconductor version: Release (3.13) Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq ...

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Detailed analysis of two brain tumor subtypes by investigators at Massachusetts General Hospital and the Broad Institute of MIT and Harvard has revealed that they may originate from the same type of neural progenitor cells and be distinguished by gene mutation patterns and by the composition of their microenvironments.

Deep sequencing analysis of gene expression (DSAGE) measures global gene transcript levels from only 1 to 2 µg total RNA by massively parallel sequencing of cDNA tags

The SePIA workflow allows analysis of paired-end total RNA, poly(A)-derived RNA, and single-end small RNA data. All three data types were processed by the same RNA-seq components at common steps in the workflow, such as preprocessing and alignment, to produce standardized outputs. The ease in which SePIAs pipelines can be extended was demonstrated in Case I, where variant calling was performed as an additional, independently-executed analysis of transcript-level data. Integrated analysis was demonstrated in Case II, identifying biologically interesting miRNA-mRNA pairs by combining expression correlation with in silico target predictions [24]. Though Cufflinks and HTSeq are used in the workflow for mapped read quantification, users may prefer other tools such as RSEM [50], eXpress [51], and BitSeq [52]. These are available as components that can also be used with SePIA and instructions on how to implement them is given on the SePIA website. Users with a basic proficiency in a programming ...

1 were set as the cut‑off criteria to screen for differentially expressed genes (DEGs). Differential gene co‑expression analysis was performed using R/EBcoexpress package in R. DEGs in the gene co‑expression network were subjected to Gene Ontology analysis using the Database for Annotation, Visualization and Integration Discovery. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was also performed on the DEGs using KOBAS 2.0 software. The ConnectivityMap database was used to identify novel drug candidates. A total of 3,742 DEGs were identified among the 552 UCEC samples and 35 normal controls, and comprised 2,580 upregulated and 1,162 downregulated genes. A gene co‑expression network consisting of 129 DEGs and 368 edges was constructed. Genes were associated with the cell cycle and the tumor protein p53 signaling pathway. Three modules were identified, in which genes were associated with the mitotic cell cycle, nuclear division and the M phase of the mitotic cell ...

Deep sequencing reveals distinct patterns of DNA methylation in prostate cancer. https://edrn.jpl.nasa.gov/publications/21724842-deep-sequencing-reveals-distinct-patterns https://edrn.jpl.nasa.gov/logo.png ...

These scripts assess differential expression between monogamous and non-monogamous species between all clades and across evolutionary subgroupings. Different approaches to differential expression analysis were used to identify novel candidates associated with monogamy. --RRHO.html: R script that performs the Rank Rank Hypergeometric Overlap (RRHO) between all pairwise clades, generates lists of most up- and down-regulated OGGs, and generates the RRHO and summary plots --DESeq2_volcanoplots.html: R script that performs DESeq2 of different evolutionary subgroups and generates volcano plots. --NovelCandidates.html: Integrate Log2 fold difference, DESeq2 all vertebrates, and RRHO most up- and down-regulated genes to generate a heatmap of novel candidates

Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl.. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database ...