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Next-generation sequencing (NGS) has revolutionized genomic research by enabling high-throughput, cost-effective genome and transcriptome sequencing accelerating personalized medicine for complex diseases, including cancer. Whole genome/transcriptome sequencing (WGS/WTS) provides comprehensive insights, while targeted sequencing is more cost-effective and sensitive. In comparison to short-read sequencing, which still dominates the field due to high speed and cost-effectiveness, long-read sequencing can overcome alignment limitations and better discriminate similar sequences from alternative transcripts or repetitive regions. Hybrid sequencing combines the best strengths of different technologies for a more comprehensive view of genomic/transcriptomic variations. Understanding each technology's strengths and limitations is critical for translating cutting-edge technologies into clinical applications. In this study, we sequenced DNA and RNA libraries of reference samples using various targeted DNA and RNA panels and the whole transcriptome on both short-read and long-read platforms. This study design enables a comprehensive analysis of sequencing technologies, targeting protocols, and library preparation methods. Our expanded profiling landscape establishes a reference point for assessing current sequencing technologies, facilitating informed decision-making in genomic research and precision medicine., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings., (© 2023. The Author(s).)

The advent of civilian spaceflight challenges scientists to precisely describe the effects of spaceflight on human physiology, particularly at the molecular and cellular level. Newer, nanopore-based sequencing technologies can quantitatively map changes in chemical structure and expression at single molecule resolution across entire isoforms. We perform long-read, direct RNA nanopore sequencing, as well as Ultima high-coverage RNA-sequencing, of whole blood sampled longitudinally from four SpaceX Inspiration4 astronauts at seven timepoints, spanning pre-flight, day of return, and post-flight recovery. We report key genetic pathways, including changes in erythrocyte regulation, stress induction, and immune changes affected by spaceflight. We also present the first m 6 A methylation profiles for a human space mission, suggesting a significant spike in m 6 A levels immediately post-flight. These data and results represent the first longitudinal long-read RNA profiles and RNA modification maps for each gene for astronauts, improving our understanding of the human transcriptome's dynamic response to spaceflight., (© 2024. The Author(s).)

Taken as a classification paradigm completing the standard model, a new compact form of the SU(2/1) supergroup explains many mysterious properties of the weak interactions: the maximal breaking of parity, the fractional charges of the quarks, the cancellation of the quantum field theory anomalies, and ties together the existence of the right neutrinos and of the heavier Fermions. This compact supergroup is constructed by exponentiating the matrices representing the leptons and the quarks which form a semi-direct sum of Kac modules of the real superalgebra su(2/1,R) such that the overall trace of the $U(1)$ weak-hypercharge $Y$ vanishes. Remarkably, all the elements of this supergroup have Berezinian 1 and determinant 1. In practice, $Tr(Y)=0$ simply means that the electric charge of the hydrogen atom is zero.

Background: Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing., Results: All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden., Conclusion: This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.

We propose an extension of the Yang-Mills paradigm from Lie algebras to internal chiral superalgebras. We replace the Lie algebra-valued connection one-form A , by a superalgebra-valued polyform A ˜ mixing exterior-forms of all degrees and satisfying the chiral self-duality condition A ˜ = * A ˜ χ , where χ denotes the superalgebra grading operator. This superconnection contains Yang-Mills vectors valued in the even Lie subalgebra, together with scalars and self-dual tensors valued in the odd module, all coupling only to the charge parity CP-positive Fermions. The Fermion quantum loops then induce the usual Yang-Mills-scalar Lagrangian, the self-dual Avdeev-Chizhov propagator of the tensors, plus a new vector-scalar-tensor vertex and several quartic terms which match the geometric definition of the supercurvature. Applied to the SU(2/1) Lie-Kac simple superalgebra, which naturally classifies all the elementary particles, the resulting quantum field theory is anomaly-free and the interactions are governed by the super-Killing metric and by the structure constants of the superalgebra.

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.

A natural generalization of a Lie algebra connection, or Yang-Mills field, to the case of a Lie-Kac superalgebra, for example SU(m/n), just in terms of ordinary complex functions and differentials, is proposed. Using the chirality χ which defines the supertrace of the superalgebra: S T r ( … ) = T r ( χ … ) , we construct a covariant differential: D = χ ( d + A ) + Φ , where A is the standard even Lie-subalgebra connection 1-form and Φ a scalar field valued in the odd module. Despite the fact that Φ is a scalar, Φ anticomtes with ( χ A ) because χ anticommutes with the odd generators hidden in Φ . Hence the curvature F = DD is a superalgebra-valued linear map which respects the Bianchi identity and correctly defines a chiral parallel transport compatible with a generic Lie superalgebra structure.

At the classical level, the SU(2/1) superalgebra offers a natural description of the elementary particles: leptons and quarks massless states, graded by their chirality, fit the smallest irreducible representations of SU(2/1). Our new proposition is to pair the left/right space-time chirality with the superalgebra chirality and to study the model at the one-loop quantum level. If, despite the fact that they are non-Hermitian, we use the odd matrices of SU(2/1) to minimally couple an oriented complex Higgs scalar field to the chiral Fermions, novel anomalies occur. They affect the scalar propagators and vertices. However, these undesired new terms cancel out, together with the Adler-Bell-Jackiw vector anomalies, because the quarks compensate the leptons. The unexpected and striking consequence is that the scalar propagator must be normalized using the antisymmetric super-Killing metric and the scalar-vector vertex must use the symmetric d_aij structure constants of the superalgebra. Despite this extraordinary structure, the resulting Lagrangian is actually Hermitian.

Understanding molecular alterations associated with peripheral inflammation is a critical factor in selectively controlling acute and persistent pain. The present report employs in situ hybridization of the 2 opioid precursor mRNAs coupled with quantitative measurements of 2 peptides derived from the prodynorphin and proenkephalin precursor proteins: dynorphin A 1-8 and [Met 5 ]-enkephalin-Arg 6 -Gly 7 -Leu 8 . In dorsal spinal cord ipsilateral to the inflammation, dynorphin A 1-8 was elevated after inflammation, and persisted as long as the inflammation was sustained. Qualitative identification by high performance liquid chromatography and gel permeation chromatography revealed the major immunoreactive species in control and inflamed extracts to be dynorphin A 1-8. In situ hybridization in spinal cord after administration of the inflammatory agent, carrageenan, showed increased expression of prodynorphin (Pdyn) mRNA somatotopically in medial superficial dorsal horn neurons. The fold increase in preproenkephalin mRNA (Penk) was comparatively lower, although the basal expression is substantially higher than Pdyn. While Pdyn is not expressed in the dorsal root ganglion (DRG) in basal conditions, it can be induced by nerve injury, but not by inflammation alone. A bioinformatic meta-analysis of multiple nerve injury datasets confirmed Pdyn upregulation in DRG across different nerve injury models. These data support the idea that activation of endogenous opioids, notably dynorphin, is a dynamic indicator of persistent pain states in spinal cord and of nerve injury in DRG. PERSPECTIVE: This is a systematic, quantitative assessment of dynorphin and enkephalin peptides and mRNA in dorsal spinal cord and DRG neurons in response to peripheral inflammation and axotomy. These studies form the foundational framework for understanding how endogenous spinal opioid peptides are involved in nociceptive circuit modulation., (Copyright © 2020. Published by Elsevier Inc.)

Secreted proteins (SPs) play important roles in diverse important biological processes; however, a comprehensive and high-quality list of human SPs is still lacking. Here we identified 6,943 high-confidence human SPs (3,522 of them are novel) based on 330,427 human proteins derived from databases of UniProt, Ensembl, AceView, and RefSeq. Notably, 6,267 of 6,943 (90.3%) SPs have the supporting evidences from a large amount of mass spectrometry (MS) and RNA-seq data. We found that the SPs were broadly expressed in diverse tissues as well as human body fluid, and a significant portion of them exhibited tissue-specific expression. Moreover, 14 cancer-specific SPs that their expression levels were significantly associated with the patients' survival of eight different tumors were identified, which could be potential prognostic biomarkers. Strikingly, 89.21% of 6,943 SPs (2,927 novel SPs) contain known protein domains. Those novel SPs we mainly enriched with the known domains regarding immunity, such as Immunoglobulin V-set and C1-set domain. Specifically, we constructed a user-friendly and freely accessible database, SPRomeDB (www.unimd.org/SPRomeDB), to catalog those SPs. Our comprehensive SP identification and characterization gain insights into human secretome and provide valuable resource for future researches., (Copyright © 2019 Chen, Chen, Liu, Chen, Zhang, Li, Thierry-Mieg, Thierry-Mieg, Mattes, Ning and Shi.)

Starting from Fermat's principle of least action, which governs classical and quantum mechanics and from the theory of exterior differential forms, which governs the geometry of curved manifolds, we show how to derive the equations governing neural networks in an intrinsic, coordinate-invariant way, where the loss function plays the role of the Hamiltonian. To be covariant, these equations imply a layer metric which is instrumental in pretraining and explains the role of conjugation when using complex numbers. The differential formalism clarifies the relation of the gradient descent optimizer with Aristotelian and Newtonian mechanics. The Bayesian paradigm is then analyzed as a renormalizable theory yielding a new derivation of the Bayesian information criterion. We hope that this formal presentation of the differential geometry of neural networks will encourage some physicists to dive into deep learning and, reciprocally, that the specialists of deep learning will better appreciate the close interconnection of their subject with the foundations of classical and quantum field theory.

Background: Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. We introduce Magic-BLAST, a new aligner based on ideas from the Magic pipeline., Results: Magic-BLAST uses innovative techniques that include the optimization of a spliced alignment score and selective masking during seed selection. We evaluate the performance of Magic-BLAST to accurately map short or long sequences and its ability to discover introns on real RNA-seq data sets from PacBio, Roche and Illumina runs, and on six benchmarks, and compare it to other popular aligners. Additionally, we look at alignments of human idealized RefSeq mRNA sequences perfectly matching the genome., Conclusions: We show that Magic-BLAST is the best at intron discovery over a wide range of conditions and the best at mapping reads longer than 250 bases, from any platform. It is versatile and robust to high levels of mismatches or extreme base composition, and reasonably fast. It can align reads to a BLAST database or a FASTA file. It can accept a FASTQ file as input or automatically retrieve an accession from the SRA repository at the NCBI.

Background The trigeminal ganglion contains neurons that relay sensations of pain, touch, pressure, and many other somatosensory modalities to the central nervous system. The ganglion is also a reservoir for latent herpes virus 1 infection. To gain a better understanding of molecular factors contributing to migraine and headache, transcriptome analyses were performed on postmortem human trigeminal ganglia. Methods RNA-Seq measurements of gene expression were conducted on small sub-regions of 16 human trigeminal ganglia. The samples were also characterized for transcripts derived from viral and microbial genomes. Herpes simplex virus 1 (HSV-1) antibodies in blood were measured using the luciferase immunoprecipitation assay. Results Observed molecular heterogeneity could be explained by sampling of anatomically distinct sub-regions of the excised ganglia consistent with neurally-enriched and non-neural, i.e. Schwann cell, enriched subregions. The levels of HSV-1 transcripts detected in trigeminal ganglia correlated with blood levels of HSV-1 antibodies. Multiple migraine susceptibility genes were strongly expressed in neurally-enriched trigeminal samples, while others were expressed in blood vessels. Conclusions These data provide a comprehensive human trigeminal transcriptome and a framework for evaluation of inhomogeneous post-mortem tissues through extensive quality control and refined downstream analyses for RNA-Seq methodologies. Expression profiling of migraine susceptibility genes identified by genetic association appears to emphasize the blood vessel component of the trigeminovascular system. Other genes displayed enriched expression in the trigeminal compared to dorsal root ganglion, and in-depth transcriptomic analysis of the KCNK18 gene underlying familial migraine shows selective neural expression within two specific populations of ganglionic neurons. These data suggest that expression profiling of migraine-associated genes can extend and amplify the underlying neurobiological insights obtained from genetic association studies.

Background: Cellular function and diversity are orchestrated by complex interactions of fundamental biomolecules including DNA, RNA and proteins. Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel and unbiased measurements. Such high-throughput technologies have been extensively used to carry out broad, unbiased studies, particularly in the context of human diseases. Nevertheless, a unified analysis of the genome, epigenome, transcriptome and proteome of a single human cell type to obtain a coherent view of the complex interplay between various biomolecules has not yet been undertaken. Here, we report the first multi-omic analysis of human primary naïve CD4+ T cells isolated from a single individual., Results: Integrating multi-omics datasets allowed us to investigate genome-wide methylation and its effect on mRNA/protein expression patterns, extent of RNA editing under normal physiological conditions and allele specific expression in naïve CD4+ T cells. In addition, we carried out a multi-omic comparative analysis of naïve with primary resting memory CD4+ T cells to identify molecular changes underlying T cell differentiation. This analysis provided mechanistic insights into how several molecules involved in T cell receptor signaling are regulated at the DNA, RNA and protein levels. Phosphoproteomics revealed downstream signaling events that regulate these two cellular states. Availability of multi-omics data from an identical genetic background also allowed us to employ novel proteogenomics approaches to identify individual-specific variants and putative novel protein coding regions in the human genome., Conclusions: We utilized multiple high-throughput technologies to derive a comprehensive profile of two primary human cell types, naïve CD4+ T cells and memory CD4+ T cells, from a single donor. Through vertical as well as horizontal integration of whole genome sequencing, methylation arrays, RNA-Seq, miRNA-Seq, proteomics, and phosphoproteomics, we derived an integrated and comparative map of these two closely related immune cells and identified potential molecular effectors of immune cell differentiation following antigen encounter.

Neuroblastoma is a malignant paediatric tumour of the sympathetic nervous system. Roughly half of these tumours regress spontaneously or are cured by limited therapy. By contrast, high-risk neuroblastomas have an unfavourable clinical course despite intensive multimodal treatment, and their molecular basis has remained largely elusive. Here we have performed whole-genome sequencing of 56 neuroblastomas (high-risk, n = 39; low-risk, n = 17) and discovered recurrent genomic rearrangements affecting a chromosomal region at 5p15.33 proximal of the telomerase reverse transcriptase gene (TERT). These rearrangements occurred only in high-risk neuroblastomas (12/39, 31%) in a mutually exclusive fashion with MYCN amplifications and ATRX mutations, which are known genetic events in this tumour type. In an extended case series (n = 217), TERT rearrangements defined a subgroup of high-risk tumours with particularly poor outcome. Despite a large structural diversity of these rearrangements, they all induced massive transcriptional upregulation of TERT. In the remaining high-risk tumours, TERT expression was also elevated in MYCN-amplified tumours, whereas alternative lengthening of telomeres was present in neuroblastomas without TERT or MYCN alterations, suggesting that telomere lengthening represents a central mechanism defining this subtype. The 5p15.33 rearrangements juxtapose the TERT coding sequence to strong enhancer elements, resulting in massive chromatin remodelling and DNA methylation of the affected region. Supporting a functional role of TERT, neuroblastoma cell lines bearing rearrangements or amplified MYCN exhibited both upregulated TERT expression and enzymatic telomerase activity. In summary, our findings show that remodelling of the genomic context abrogates transcriptional silencing of TERT in high-risk neuroblastoma and places telomerase activation in the centre of transformation in a large fraction of these tumours.

Background: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model., Results: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models., Conclusions: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

The non-human primate reference transcriptome resource (NHPRTR, available online at http://nhprtr.org/) aims to generate comprehensive RNA-seq data from a wide variety of non-human primates (NHPs), from lemurs to hominids. In the 2012 Phase I of the NHPRTR project, 19 billion fragments or 3.8 terabases of transcriptome sequences were collected from pools of ∼ 20 tissues in 15 species and subspecies. Here we describe a major expansion of NHPRTR by adding 10.1 billion fragments of tissue-specific RNA-seq data. For this effort, we selected 11 of the original 15 NHP species and subspecies and constructed total RNA libraries for the same ∼ 15 tissues in each. The sequence quality is such that 88% of the reads align to human reference sequences, allowing us to compute the full list of expression abundance across all tissues for each species, using the reads mapped to human genes. This update also includes improved transcript annotations derived from RNA-seq data for rhesus and cynomolgus macaques, two of the most commonly used NHP models and additional RNA-seq data compiled from related projects. Together, these comprehensive reference transcriptomes from multiple primates serve as a valuable community resource for genome annotation, gene dynamics and comparative functional analysis., (© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.)

High-throughput RNA sequencing (RNA-seq) enables comprehensive scans of entire transcriptomes, but best practices for analyzing RNA-seq data have not been fully defined, particularly for data collected with multiple sequencing platforms or at multiple sites. Here we used standardized RNA samples with built-in controls to examine sources of error in large-scale RNA-seq studies and their impact on the detection of differentially expressed genes (DEGs). Analysis of variations in guanine-cytosine content, gene coverage, sequencing error rate and insert size allowed identification of decreased reproducibility across sites. Moreover, commonly used methods for normalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality. Normalization methods that combine data from genes across sites are strongly recommended to identify and remove site-specific effects and can substantially improve RNA-seq studies.

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R(2)0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

Whole-transcriptome sequencing ('RNA-Seq') has been drastically changing the scale and scope of genomic research. In order to fully understand the power and limitations of this technology, the US Food and Drug Administration (FDA) launched the third phase of the MicroArray Quality Control (MAQC-III) project, also known as the SEquencing Quality Control (SEQC) project. Using two well-established human reference RNA samples from the first phase of the MAQC project, three sequencing platforms were tested across more than ten sites with built-in truths including spike-in of external RNA controls (ERCC), titration data and qPCR verification. The SEQC project generated over 30 billion sequence reads representing the largest RNA-Seq data ever generated by a single project on individual RNA samples. This extraordinarily ultradeep transcriptomic data set and the known truths built into the study design provide many opportunities for further research and development to advance the improvement and application of RNA-Seq.

RNA-Seq provides the capability to characterize the entire transcriptome in multiple levels including gene expression, allele specific expression, alternative splicing, fusion gene detection, and etc. The US FDA-led SEQC (i.e., MAQC-III) project conducted a comprehensive study focused on the transcriptome profiling of rat liver samples treated with 27 chemicals to evaluate the utility of RNA-Seq in safety assessment and toxicity mechanism elucidation. The chemicals represented multiple chemogenomic modes of action (MOA) and exhibited varying degrees of transcriptional response. The paired-end 100 bp sequencing data were generated using Illumina HiScanSQ and/or HiSeq 2000. In addition to the core study, six animals (i.e., three aflatoxin B1 treated rats and three vehicle control rats) were sequenced three times, with two separate library preparations on two sequencing machines. This large toxicogenomics dataset can serve as a resource to characterize various aspects of transcriptomic changes (e.g., alternative splicing) that are byproduct of chemical perturbation.

Background: Three neuropeptides, gastrin releasing peptide (GRP), natriuritic precursor peptide B (NPPB), and neuromedin B (NMB) have been proposed to play roles in itch sensation. However, the tissues in which these peptides are expressed and their positions in the itch circuit has recently become the subject of debate. Here we used next-gen RNA-Seq to examine the expression of transcripts coding for GRP, NPPB, NMB, and other peptides in DRG, trigeminal ganglion, and the spinal cord as well as expression levels for their cognate receptors in these tissues., Results: RNA-Seq demonstrates that GRP is not transcribed in mouse, rat, or human sensory ganglia. NPPB, which activates natriuretic peptide receptor 1 (NPR1), is well expressed in mouse DRG and less so in rat and human, whereas NPPA, which also acts on the NPR1 receptor, is expressed in all three species. Analysis of transcripts expressed in the spinal cord of mouse, rat, and human reveals no expression of Nppb, but unambiguously detects expression of Grp and the GRP-receptor (Grpr). The transcripts coding for NMB and tachykinin peptides are among the most highly expressed in DRG. Bioinformatics comparisons using the sequence of the peptides used to produce GRP-antibodies with proteome databases revealed that the C-terminal primary sequence of NMB and Substance P can potentially account for results from previous studies which showed GRP-immunostaining in the DRG., Conclusions: RNA-Seq corroborates a primary itch afferent role for NPPB in mouse and potentially NPPB and NPPA in rats and humans, but does not support GRP as a primary itch neurotransmitter in mouse, rat, or humans. As such, our results are at odds with the initial proposal of Sun and Chen (2007) that GRP is expressed in DRG. By contrast, our data strongly support an itch pathway where the itch-inducing actions of GRP are exerted through its release from spinal cord neurons.

Unlabelled: Due to the size of Next-Generation Sequencing data, the computational challenge of sequence alignment has been vast. Inexact alignments can take up to 90% of total CPU time in bioinformatics pipelines. High-performance Integrated Virtual Environment (HIVE), a cloud-based environment optimized for storage and analysis of extra-large data, presents an algorithmic solution: the HIVE-hexagon DNA sequence aligner. HIVE-hexagon implements novel approaches to exploit both characteristics of sequence space and CPU, RAM and Input/Output (I/O) architecture to quickly compute accurate alignments. Key components of HIVE-hexagon include non-redundification and sorting of sequences; floating diagonals of linearized dynamic programming matrices; and consideration of cross-similarity to minimize computations., Availability: https://hive.biochemistry.gwu.edu/hive/

The rat has been used extensively as a model for evaluating chemical toxicities and for understanding drug mechanisms. However, its transcriptome across multiple organs, or developmental stages, has not yet been reported. Here we show, as part of the SEQC consortium efforts, a comprehensive rat transcriptomic BodyMap created by performing RNA-Seq on 320 samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats. We catalogue the expression profiles of 40,064 genes, 65,167 transcripts, 31,909 alternatively spliced transcript variants and 2,367 non-coding genes/non-coding RNAs (ncRNAs) annotated in AceView. We find that organ-enriched, differentially expressed genes reflect the known organ-specific biological activities. A large number of transcripts show organ-specific, age-dependent or sex-specific differential expression patterns. We create a web-based, open-access rat BodyMap database of expression profiles with crosslinks to other widely used databases, anticipating that it will serve as a primary resource for biomedical research using the rat model.

RNA-based next-generation sequencing (RNA-Seq) provides a tremendous amount of new information regarding gene and transcript structure, expression and regulation. This is particularly true for non-coding RNAs where whole transcriptome analyses have revealed that the much of the genome is transcribed and that many non-coding transcripts have widespread functionality. However, uniform resources for raw, cleaned and processed RNA-Seq data are sparse for most organisms and this is especially true for non-human primates (NHPs). Here, we describe a large-scale RNA-Seq data and analysis infrastructure, the NHP reference transcriptome resource (http://nhprtr.org); it presently hosts data from12 species of primates, to be expanded to 15 species/subspecies spanning great apes, old world monkeys, new world monkeys and prosimians. Data are collected for each species using pools of RNA from comparable tissues. We provide data access in advance of its deposition at NCBI, as well as browsable tracks of alignments against the human genome using the UCSC genome browser. This resource will continue to host additional RNA-Seq data, alignments and assemblies as they are generated over the coming years and provide a key resource for the annotation of NHP genomes as well as informing primate studies on evolution, reproduction, infection, immunity and pharmacology.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Three-prime untranslated regions (3'UTRs) of metazoan messenger RNAs (mRNAs) contain numerous regulatory elements, yet remain largely uncharacterized. Using polyA capture, 3' rapid amplification of complementary DNA (cDNA) ends, full-length cDNAs, and RNA-seq, we defined approximately 26,000 distinct 3'UTRs in Caenorhabditis elegans for approximately 85% of the 18,328 experimentally supported protein-coding genes and revised approximately 40% of gene models. Alternative 3'UTR isoforms are frequent, often differentially expressed during development. Average 3'UTR length decreases with animal age. Surprisingly, no polyadenylation signal (PAS) was detected for 13% of polyadenylation sites, predominantly among shorter alternative isoforms. Trans-spliced (versus non-trans-spliced) mRNAs possess longer 3'UTRs and frequently contain no PAS or variant PAS. We identified conserved 3'UTR motifs, isoform-specific predicted microRNA target sites, and polyadenylation of most histone genes. Our data reveal a rich complexity of 3'UTRs, both genome-wide and throughout development.

Interleukin-21 (IL-21) is a pleiotropic cytokine that induces expression of transcription factor BLIMP1 (encoded by Prdm1), which regulates plasma cell differentiation and T cell homeostasis. We identified an IL-21 response element downstream of Prdm1 that binds the transcription factors STAT3 and IRF4, which are required for optimal Prdm1 expression. Genome-wide ChIP-Seq mapping of STAT3- and IRF4-binding sites showed that most regions with IL-21-induced STAT3 binding also bound IRF4 in vivo and furthermore revealed that the noncanonical TTCnnnTAA GAS motif critical in Prdm1 was broadly used for STAT3 binding. Comparing genome-wide expression array data to binding sites revealed that most IL-21-regulated genes were associated with combined STAT3-IRF4 sites rather than pure STAT3 sites. Correspondingly, ChIP-Seq analysis of Irf4(-/-) T cells showed greatly diminished STAT3 binding after IL-21 treatment, and Irf4(-/-) mice showed impaired IL-21-induced Tfh cell differentiation in vivo. These results reveal broad cooperative gene regulation by STAT3 and IRF4., (Copyright 2009 Elsevier Inc. All rights reserved.)

The organization of mammalian DNA replication is poorly understood. We have produced high-resolution dynamic maps of the timing of replication in human erythroid, mesenchymal, and embryonic stem (ES) cells using TimEX, a method that relies on gaussian convolution of massive, highly redundant determinations of DNA copy-number variations during S phase to produce replication timing profiles. We first obtained timing maps of 3% of the genome using high-density oligonucleotide tiling arrays and then extended the TimEX method genome-wide using massively parallel sequencing. We show that in untransformed human cells, timing of replication is highly regulated and highly synchronous, and that many genomic segments are replicated in temporal transition regions devoid of initiation, where replication forks progress unidirectionally from origins that can be hundreds of kilobases away. Absence of initiation in one transition region is shown at the molecular level by single molecule analysis of replicated DNA (SMARD). Comparison of ES and erythroid cells replication patterns revealed that these cells replicate about 20% of their genome in different quarters of S phase. Importantly, we detected a strong inverse relationship between timing of replication and distance to the closest expressed gene. This relationship can be used to predict tissue-specific timing of replication profiles from expression data and genomic annotations. We also provide evidence that early origins of replication are preferentially located near highly expressed genes, that mid-firing origins are located near moderately expressed genes, and that late-firing origins are located far from genes.

Endogenous small interfering RNAs (endo-siRNAs) regulate diverse gene expression programs in eukaryotes by either binding and cleaving mRNA targets or mediating heterochromatin formation; however, the mechanisms of endo-siRNA biogenesis, sorting, and target regulation remain poorly understood. Here we report the identification and function of a specific class of germline-generated endo-siRNAs in Caenorhabditis elegans that are 26 nt in length and contain a guanine at the first nucleotide position (i.e., 26G RNAs). 26G RNAs regulate gene expression during spermatogenesis and zygotic development, and their biogenesis requires the ERI-1 exonuclease and the RRF-3 RNA-dependent RNA polymerase (RdRP). Remarkably, we identified two nonoverlapping subclasses of 26G RNAs that sort into specific RNA-induced silencing complexes (RISCs) and differentially regulate distinct mRNA targets. Class I 26G RNAs target genes are expressed during spermatogenesis, whereas class II 26G RNAs are maternally inherited and silence gene expression during zygotic development. These findings implicate a class of endo-siRNAs in the global regulation of transcriptional programs required for fertility and development.

Background: Transcriptome sequencing using next-generation sequencing platforms will soon be competing with DNA microarray technologies for global gene expression analysis. As a preliminary evaluation of these promising technologies, we performed deep sequencing of cDNA synthesized from the Microarray Quality Control (MAQC) reference RNA samples using Roche's 454 Genome Sequencer FLX., Results: We generated more that 3.6 million sequence reads of average length 250 bp for the MAQC A and B samples and introduced a data analysis pipeline for translating cDNA read counts into gene expression levels. Using BLAST, 90% of the reads mapped to the human genome and 64% of the reads mapped to the RefSeq database of well annotated genes with e-values

Here we report the new features and improvements in our latest release of the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/), a comprehensive annotation resource for human genes and transcripts. H-InvDB, originally developed as an integrated database of the human transcriptome based on extensive annotation of large sets of full-length cDNA (FLcDNA) clones, now provides annotation for 120 558 human mRNAs extracted from the International Nucleotide Sequence Databases (INSD), in addition to 54 978 human FLcDNAs, in the latest release H-InvDB_4.6. We mapped those human transcripts onto the human genome sequences (NCBI build 36.1) and determined 34 699 human gene clusters, which could define 34 057 (98.1%) protein-coding and 642 (1.9%) non-protein-coding loci; 858 (2.5%) transcribed loci overlapped with predicted pseudogenes. For all these transcripts and genes, we provide comprehensive annotation including gene structures, gene functions, alternative splicing variants, functional non-protein-coding RNAs, functional domains, predicted sub cellular localizations, metabolic pathways, predictions of protein 3D structure, mapping of SNPs and microsatellite repeat motifs, co-localization with orphan diseases, gene expression profiles, orthologous genes, protein-protein interactions (PPI) and annotation for gene families. The current H-InvDB annotation resources consist of two main views: Transcript view and Locus view and eight sub-databases: the DiseaseInfo Viewer, H-ANGEL, the Clustering Viewer, G-integra, the TOPO Viewer, Evola, the PPI view and the Gene family/group.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

We have assessed the utility of RNA titration samples for evaluating microarray platform performance and the impact of different normalization methods on the results obtained. As part of the MicroArray Quality Control project, we investigated the performance of five commercial microarray platforms using two independent RNA samples and two titration mixtures of these samples. Focusing on 12,091 genes common across all platforms, we determined the ability of each platform to detect the correct titration response across the samples. Global deviations from the response predicted by the titration ratios were observed. These differences could be explained by variations in relative amounts of messenger RNA as a fraction of total RNA between the two independent samples. Overall, both the qualitative and quantitative correspondence across platforms was high. In summary, titration samples may be regarded as a valuable tool, not only for assessing microarray platform performance and different analysis methods, but also for determining some underlying biological features of the samples.

Background: Regions covering one percent of the genome, selected by ENCODE for extensive analysis, were annotated by the HAVANA/Gencode group with high quality transcripts, thus defining a benchmark. The ENCODE Genome Annotation Assessment Project (EGASP) competition aimed at reproducing Gencode and finding new genes. The organizers evaluated the protein predictions in depth. We present a complementary analysis of the mRNAs, including alternative transcript variants., Results: We evaluate 25 gene tracks from the University of California Santa Cruz (UCSC) genome browser. We either distinguish or collapse the alternative splice variants, and compare the genomic coordinates of exons, introns and nucleotides. Whole mRNA models, seen as chains of introns, are sorted to find the best matching pairs, and compared so that each mRNA is used only once. At the mRNA level, AceView is by far the closest to Gencode: the vast majority of transcripts of the two methods, including alternative variants, are identical. At the protein level, however, due to a lack of experimental data, our predictions differ: Gencode annotates proteins in only 41% of the mRNAs whereas AceView does so in virtually all. We describe the driving principles of AceView, and how, by performing hand-supervised automatic annotation, we solve the combinatorial splicing problem and summarize all of GenBank, dbEST and RefSeq into a genome-wide non-redundant but comprehensive cDNA-supported transcriptome. AceView accuracy is now validated by Gencode., Conclusion: Relative to a consensus mRNA catalog constructed from all evidence-based annotations, Gencode and AceView have 81% and 84% sensitivity, and 74% and 73% specificity, respectively. This close agreement validates a richer view of the human transcriptome, with three to five times more transcripts than in UCSC Known Genes (sensitivity 28%), RefSeq (sensitivity 21%) or Ensembl (sensitivity 19%).

We report the first genome-wide identification and characterization of alternative splicing in human gene transcripts based on analysis of the full-length cDNAs. Applying both manual and computational analyses for 56,419 completely sequenced and precisely annotated full-length cDNAs selected for the H-Invitational human transcriptome annotation meetings, we identified 6877 alternative splicing genes with 18 297 different alternative splicing variants. A total of 37,670 exons were involved in these alternative splicing events. The encoded protein sequences were affected in 6005 of the 6877 genes. Notably, alternative splicing affected protein motifs in 3015 genes, subcellular localizations in 2982 genes and transmembrane domains in 1348 genes. We also identified interesting patterns of alternative splicing, in which two distinct genes seemed to be bridged, nested or having overlapping protein coding sequences (CDSs) of different reading frames (multiple CDS). In these cases, completely unrelated proteins are encoded by a single locus. Genome-wide annotations of alternative splicing, relying on full-length cDNAs, should lay firm groundwork for exploring in detail the diversification of protein function, which is mediated by the fast expanding universe of alternative splicing variants.

The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology., Competing Interests: The authors have declared that no conflicts of interest exist.

The nematode worm Caenorhabditis elegans and its relatives are unique among animals in having operons. Operons are regulated multigene transcription units, in which polycistronic pre-messenger RNA (pre-mRNA coding for multiple peptides) is processed to monocistronic mRNAs. This occurs by 3' end formation and trans-splicing using the specialized SL2 small nuclear ribonucleoprotein particle for downstream mRNAs. Previously, the correlation between downstream location in an operon and SL2 trans-splicing has been strong, but anecdotal. Although only 28 operons have been reported, the complete sequence of the C. elegans genome reveals numerous gene clusters. To determine how many of these clusters represent operons, we probed full-genome microarrays for SL2-containing mRNAs. We found significant enrichment for about 1,200 genes, including most of a group of several hundred genes represented by complementary DNAs that contain SL2 sequence. Analysis of their genomic arrangements indicates that >90% are downstream genes, falling in 790 distinct operons. Our evidence indicates that the genome contains at least 1,000 operons, 2 8 genes long, that contain about 15% of all C. elegans genes. Numerous examples of co-transcription of genes encoding functionally related proteins are evident. Inspection of the operon list should reveal previously unknown functional relationships.

The genome sequences of Caenorhabditis elegans, Drosophila melanogaster and Arabidopsis thaliana have been predicted to contain 19,000, 13,600 and 25,500 genes, respectively. Before this information can be fully used for evolutionary and functional studies, several issues need to be addressed. First, the gene number estimates obtained in silico and not yet supported by any experimental data need to be verified. For example, it seems biologically paradoxical that C. elegans would have 50% more genes than Drosophilia. Second, intron/exon predictions need to be tested experimentally. Third, complete sets of open reading frames (ORFs), or "ORFeomes," need to be cloned into various expression vectors. To address these issues simultaneously, we have designed and applied to C. elegans the following strategy. Predicted ORFs are amplified by PCR from a highly representative cDNA library using ORF-specific primers, cloned by Gateway recombination cloning and then sequenced to generate ORF sequence tags (OSTs) as a way to verify identity and splicing. In a sample (n=1,222) of the nearly 10,000 genes predicted ab initio (that is, for which no expressed sequence tag (EST) is available so far), at least 70% were verified by OSTs. We also observed that 27% of these experimentally confirmed genes have a structure different from that predicted by GeneFinder. We now have experimental evidence that supports the existence of at least 17,300 genes in C. elegans. Hence we suggest that gene counts based primarily on ESTs may underestimate the number of genes in human and in other organisms.

The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

WormBase (http://www.wormbase.org) is a web-based resource for the Caenorhabditis elegans genome and its biology. It builds upon the existing ACeDB database of the C.elegans genome by providing data curation services, a significantly expanded range of subject areas and a user-friendly front end.

In vertebrates, Fibroblast Growth Factors (FGFs) and their receptors are involved in various developmental and pathological processes, including neoplasia. The number of FGFs and their large range of activities have made the understanding of their precise functions difficult. Investigating their biology in other species might be enlightening. A sequence encoding a putative protein presenting 30-40% identity with the conserved core of vertebrate FGFs has been identified by the C. elegans sequencing consortium. We show here that this gene is transcribed and encodes a putative protein of 425 amino acids (aa). The gene is expressed at all stages of development beyond late embryogenesis, peaking at the larval stages. Loss-of-function mutants of the let-756 gene are rescued by the wild type fgf gene in germline transformation experiments. Two partial loss-of-function alleles, s2613 and s2809, have a mutation that replaces aa 317 by a stop. The truncated protein retains the FGF core but lacks a C-termins portion. These worms are small and develop slowly into clear and scrawny, yet viable and fertile adults. A third allele, s2887, is inactivated by an inversion that disrupts the first exon. It causes a developmental arrest early in the larval stages. Thus, in contrast to the other nematode fgf gene egl-17, let-756/fgf is essential for worm development.

Much of the world's genomic data are available to the community through networked databases that are accessed via Web interfaces. Although this paradigm provides browse-level access and has greatly facilitated linking between databases, it does not provide any convenient mechanism for programmatically fetching and integrating data from diverse databases. We have created a library and an application programming interface (API) named AcePerl that provides simple, direct access to ACEDB databases from the Perl programming language. With this library, programmers and computer-savvy biologists can write software to pose complex queries on local and remote ACEDB databases, retrieve the data, integrate the results, and move data objects from one database to another. In addition, a set of Web scripts running on top of AcePerl provides Web-based browsing of any local or remote ACEDB database. AcePerl and the AceBrowser Web browser run on Unix systems and are available under a license that allows for unrestricted use and redistribution. Both packages can be downloaded from URL. A Microsoft Windows port of AcePerl is in the planning stages.

To achieve the integration of biological data available on the World Wide Web and maintained in diverse sources such as GDB, Genbank or Acedb, we have developed a software called Jade. Jade allows programmers to create analytic tools and graphical user interfaces for one or more existing bioinformatics data sources. These tools can then be interchanged, compared and reused without making modifications in the data sources themselves. The system is implemented in the Java programming language and will run equally well on Macintosh, Windows or Unix workstations. Jade is free and can be used immediately by all interested parties.

To achieve the integration of biological data available on the World Wide Web and maintained in diverse sources such as GDB, Genbank or Acedb, we have developed a software called Jade. Jade allows programmers to create analytic tools and graphical user interfaces for one or more existing bioinformatics data sources. These tools can then be interchanged, compared and reused without making modifications in the data sources themselves. The system is implemented in the Java programming language and will run equally well on Macintosh, Windows or Unix workstations. Jade is free and can be used immediately by all interested parties., (Copyright 1998 Elsevier Science B.V.)

Large-scale genomic sequencing requires a software infrastructure to support and integrate applications that are not directly compatible. We describe a suite of software tools built around the Common Assembly Format (CAF), a comprehensive representation of a sequence assembly as a text file. These tools form the backbone of sequencing informatics at the Sanger Centre and the Genome Sequencing Center. The CAF format is intentionally flexible, and our Perl and C libraries, which parse and manipulate it, provide powerful tools for creating new applications as well as wrappers to incorporate other software. The tools are available free by anonymous FTP from ftp://ftp.sanger.ac.uk/pub/badger/.

The long-term goal of this project is the elucidation of the complete sequence of the Caenorhabditis elegans genome. During the first year methods have been developed and a strategy implemented that is amenable to large-scale sequencing. The three cosmids sequenced in this initial phase are surprisingly rich in genes, many of which have mammalian homologues.