Short-read RNA Sequencing Data Research Articles

Transcriptome Responses to Different Salinity Conditions in Litoditis marina, Revealed by Long-Read Sequencing.

The marine nematode Litoditis marina is widely distributed in intertidal zones around the globe, yet the mechanisms underlying its broad adaptation to salinity remain elusive. In this study, we applied ONT long-read sequencing technology to unravel the transcriptome responses to different salinity conditions in L. marina. Through ONT sequencing under 3‱, 30‱ and 60‱ salinity environments, we obtained 131.78 G clean data and 26,647 non-redundant long-read transcripts, including 6464 novel transcripts. The DEGs obtained from the current ONT lrRNA-seq were highly correlated with those identified in our previously reported Illumina short-read RNA sequencing data. When we compared the 30‱ to the 3‱ salinity condition, we found that GO terms such as oxidoreductase activity, cation transmembrane transport and ion transmembrane transport were shared between the ONT lrRNA-seq and Illumina data. Similarly, GO terms including extracellular space, structural constituents of cuticle, substrate-specific channel activity, ion transport and substrate-specific transmembrane transporter activity were shared between the ONT and Illumina data under 60‱ compared to 30‱ salinity. In addition, we found that 79 genes significantly increased, while 119 genes significantly decreased, as the salinity increased. Furthermore, through the GO enrichment analysis of 214 genes containing DAS, in 30‱ compared to 3‱ salinity, we found that GO terms such as cellular component assembly and coenzyme biosynthetic process were enriched. Additionally, we observed that GO terms such as cellular component assembly and coenzyme biosynthetic process were also enriched in 60‱ compared to 30‱ salinity. Moreover, we found that 86, 125, and 81 genes that contained DAS were also DEGs, in comparisons between 30‱ and 3‱, 60‱ and 30‱, and 60‱ and 3‱ salinity, respectively. In addition, we demonstrated the landscape of alternative polyadenylation in marine nematode under different salinity conditions This report provides several novel insights for the further study of the mechanisms by which euryhalinity formed and evolved, and it might also contribute to the investigation of salinity dynamics induced by global climate change.

The roles of different gene expression regulators in acoustic variation in the intermediate horseshoe bat revealed by long-read and short-read RNA sequencing data.

Gene expression changes contribute greatly to phenotypic variations in nature. Studying patterns of regulators of gene expression is important to fully understand the molecular mechanism underlying phenotypic variations. In horseshoe bats, the cochleae are finely tuned to echoes of call frequency. Here, using 2 recently diverged subspecies of the intermediate horseshoe bat (Rhinolophus affinis hainanus and R. a. himalayanus) with great acoustic variations as the system, we aim to explore relative roles of different regulators of gene expression (differential gene expression, alternative splicing (AS) and long non-coding RNAs (lncRNAs)) in phenotypic variation with a combination of Illumina short-read and Nanopore long-read RNA-seq data from the cochlea. Compared to R. a. hainanus, R. a. himalayanus exhibited much more upregulated differentially expressed genes (DEGs) and multiple of them may play important roles in the maintenance and damage repair of auditory hair cells. We identified 411 differentially expressed lncRNAs and their target DEGs upregulated in R. a. himalayanus were also mainly involved in a protective mechanism for auditory hair cells. Using 3 different methods of AS analysis, we identified several candidate alternatively spliced genes (ASGs) that expressed different isoforms which may be associated with acoustic divergence of the 2 subspecies. We observed significantly less overlap than expected between DEGs and ASGs, supporting complementary roles of differential gene expression and AS in generating phenotypic variations. Overall, our study highlights the importance of a combination of short-read and long-read RNA-seq data in examining the regulation of gene expression changes responsible for phenotypic variations.

FLIBase: a comprehensive repository of full-length isoforms across human cancers and tissues.

Regulatory processes at the RNA transcript level play a crucial role in generating transcriptome diversity and proteome composition in human cells, impacting both physiological and pathological states. This study introduces FLIBase (www.FLIBase.org), a specialized database that focuses on annotating full-length isoforms using long-read sequencing techniques. We collected and integrated long-read (351 samples) and short-read (12469 samples) RNA sequencing data from diverse normal and cancerous human tissues and cells. The current version of FLIBase comprises a total of 983789 full-length spliced isoforms, identified through long-read sequences and verified using short-read exon-exon splice junctions. Of these, 188248 isoforms have been annotated, while 795541 isoforms remain unannotated. By overcoming the limitations of short-read RNA sequencing methods, FLIBase provides an accurate and comprehensive representation of full-length transcripts. These comprehensive annotations empower researchers to undertake various downstream analyses and investigations. Importantly, FLIBase exhibits a significant advantage in identifying a substantial number of previously unannotated isoforms and tumor-specific RNA transcripts. These tumor-specific RNA transcripts have the potential to serve as a source of immunogenic recurrent neoantigens. This remarkable discovery holds tremendous promise for advancing the development of tailored RNA-based diagnostic and therapeutic strategies for various types of human cancer.

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision.

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.

Expression of alternative transcription factor 4 mRNAs and protein isoforms in the developing and adult rodent and human tissues.

Transcription factor 4 (TCF4) belongs to the class I basic helix-loop-helix family of transcription factors (also known as E-proteins) and is vital for the development of the nervous system. Aberrations in the TCF4 gene are associated with several neurocognitive disorders such as schizophrenia, intellectual disability, post-traumatic stress disorder, depression, and Pitt-Hopkins Syndrome, a rare but severe autism spectrum disorder. Expression of the human TCF4 gene can produce at least 18 N-terminally distinct protein isoforms, which activate transcription with different activities and thus may vary in their function during development. We used long-read RNA-sequencing and western blot analysis combined with the analysis of publicly available short-read RNA-sequencing data to describe both the mRNA and protein expression of the many distinct TCF4 isoforms in rodent and human neural and nonneural tissues. We show that TCF4 mRNA and protein expression is much higher in the rodent brain compared to nonneural tissues. TCF4 protein expression is highest in the rodent cerebral cortex and hippocampus, where expression peaks around birth, and in the rodent cerebellum, where expression peaks about a week after birth. In human, highest TCF4 expression levels were seen in the developing brain, although some nonneural tissues displayed comparable expression levels to adult brain. In addition, we show for the first time that out of the many possible TCF4 isoforms, the main TCF4 isoforms expressed in the rodent and human brain and other tissues are TCF4-B, -C, -D, -A, and-I. Taken together, our isoform specific analysis of TCF4 expression in different tissues could be used for the generation of gene therapy applications for patients with TCF4-associated diseases.

A chromosome-level genome of the human blood fluke Schistosoma japonicum identifies the genomic basis of host-switching.

The evolution and adaptation of S.japonicum, a zoonotic parasite that causes human schistosomiasis, remain unclear because of the lack of whole-genome data. We construct a chromosome-level S.japonicum genome and analyze it together with 72 samples representing six populations of the entire endemic region. We observe a Taiwan zoophilic lineage splitting from zoonotic populations ∼45,000 years ago, consistent with the divergent history of their intermediate hosts. Interestingly, we detect a severe population bottleneck in S.japonicum, largely coinciding with human history in Asia during the last glacial maximum. We identify several genomic regions underlying natural selection, including GATAD2A and Lmln, both showing remarkable differentiation among different areas. RNAi knockdown suggests association of GATAD2A with parasite development and infection in definitive hosts, while Lmln relates to the specificity of the intermediate hosts. Our study provides insights into the evolution of S.japonicum and serves as a resource for further studies.

Evaluation of methods to detect circular RNAs from single-end RNA-sequencing data

BackgroundCircular RNA (circRNA), a class of RNA molecule with a loop structure, has recently attracted researchers due to its diverse biological functions and potential biomarkers of human diseases. Most of the current circRNA detection methods from RNA-sequencing (RNA-Seq) data utilize the mapping information of paired-end (PE) reads to eliminate false positives. However, much of the practical RNA-Seq data such as cross-linking immunoprecipitation sequencing (CLIP-Seq) data usually contain single-end (SE) reads. It is not clear how well these tools perform on SE RNA-Seq data.ResultsIn this study, we present a systematic evaluation of six advanced RNA-based methods and two CLIP-Seq based methods for detecting circRNAs from SE RNA-Seq data. The performances of the methods are rigorously assessed based on precision, sensitivity, F1 score, and true discovery rate. We investigate the impacts of read length, false positive ratio, sequencing depth and PE mapping information on the performances of the methods using simulated SE RNA-Seq simulated datasets. The real datasets used in this study consist of four experimental RNA-Seq datasets with ≥100bp read length and 124 CLIP-Seq samples from 45 studies that contain mostly short-read (≤50bp) RNA-Seq data. The simulation study shows that the sensitivities of most of the methods can be improved by increasing either read length or sequencing depth, and that the levels of false positive rates significantly affect the precision of all methods. Furthermore, the PE mapping information can improve the method’s precision but can not always guarantee the increase of F1 score. Overall, no method is dominant for all SE RNA-Seq data. The RNA-based methods perform better for the long-read datasets but are worse for the short-read datasets. In contrast, the CLIP-Seq based methods outperform the RNA-Seq based methods for all the short-read samples. Combining the results of these methods can significantly improve precision in the CLIP-Seq data.ConclusionsThe results provide a systematic evaluation of circRNA detection methods on SE RNA-Seq data that would facilitate researchers’ strategies in circRNA analysis.

Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data.

The accuracy of methods for assembling transcripts from short-read RNA sequencing data is limited by the lack of long-range information. Here we introduce Ladder-seq, an approach that separates transcripts according to their lengths before sequencing and uses the additional information to improve the quantification and assembly of transcripts. Using simulated data, we show that a kallisto algorithm extended to process Ladder-seq data quantifies transcripts of complex genes with substantially higher accuracy than conventional kallisto. For reference-based assembly, a tailored scheme based on the StringTie2 algorithm reconstructs a single transcript with 30.8% higher precision than its conventional counterpart and is more than 30% more sensitive for complex genes. For de novo assembly, a similar scheme based on the Trinity algorithm correctly assembles 78% more transcripts than conventional Trinity while improving precision by 78%. In experimental data, Ladder-seq reveals 40% more genes harboring isoform switches compared to conventional RNA sequencing and unveils widespread changes in isoform usage upon m6A depletion by Mettl14 knockout.

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

Long-read isoform sequencing reveals tissue-specific isoform expression between active and hibernating brown bears (Ursus arctos).

Understanding hibernation in brown bears (Ursus arctos) can provide insight into some human diseases. During hibernation, brown bears experience periods of insulin resistance, physical inactivity, extreme bradycardia, obesity, and the absence of urine production. These states closely mimic aspects of human diseases such as type 2 diabetes, muscle atrophy, as well as renal and heart failure. The reversibility of these states from hibernation to active season enables the identification of mediators with possible therapeutic value for humans. Recent studies have identified genes and pathways that are differentially expressed between active and hibernation seasons in bears. However, little is known about the role of differential expression of gene isoforms on hibernation physiology. To identify both distinct and novel mRNA isoforms, full-length RNA-sequencing (Iso-Seq) was performed on adipose, skeletal muscle, and liver from three individual bears sampled during both active and hibernation seasons. The existing reference genome annotation was improved by combining it with the Iso-Seq data. Short-read RNA-sequencing data from six individuals were mapped to the new reference annotation to quantify differential isoform usage (DIU) between tissues and seasons. We identified differentially expressed isoforms in all three tissues, to varying degrees. Adipose had a high level of DIU with isoform switching, regardless of whether the genes were differentially expressed. Our analyses revealed that DIU, even in the absence of differential gene expression, is an important mechanism for modulating genes during hibernation. These findings demonstrate the value of isoform expression studies and will serve as the basis for deeper exploration into hibernation biology.

Transposable element sequence fragments incorporated into coding and noncoding transcripts modulate the transcriptome of human pluripotent stem cells.

Transposable elements (TEs) occupy nearly 40% of mammalian genomes and, whilst most are fragmentary and no longer capable of transposition, they can nevertheless contribute to cell function. TEs within genes transcribed by RNA polymerase II can be copied as parts of primary transcripts; however, their full contribution to mature transcript sequences remains unresolved. Here, using long and short read (LR and SR) RNA sequencing data, we show that 26% of coding and 65% of noncoding transcripts in human pluripotent stem cells (hPSCs) contain TE-derived sequences. Different TE families are incorporated into RNAs in unique patterns, with consequences to transcript structure and function. The presence of TE sequences within a transcript is correlated with TE-type specific changes in its subcellular distribution, alterations in steady-state levels and half-life, and differential association with RNA Binding Proteins (RBPs). We identify hPSC-specific incorporation of endogenous retroviruses (ERVs) and LINE:L1 into protein-coding mRNAs, which generate TE sequence-derived peptides. Finally, single cell RNA-seq reveals that hPSCs express ERV-containing transcripts, whilst differentiating subpopulations lack ERVs and express SINE and LINE-containing transcripts. Overall, our comprehensive analysis demonstrates that the incorporation of TE sequences into the RNAs of hPSCs is more widespread and has a greater impact than previously appreciated.

NASA GeneLab RNA-seq consensus pipeline: Standardized processing of short-read RNA-seq data

SummaryWith the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab.

Long-read transcriptome sequencing reveals abundant promoter diversity in distinct molecular subtypes of gastric cancer

BackgroundDeregulated gene expression is a hallmark of cancer; however, most studies to date have analyzed short-read RNA sequencing data with inherent limitations. Here, we combine PacBio long-read isoform sequencing (Iso-Seq) and Illumina paired-end short-read RNA sequencing to comprehensively survey the transcriptome of gastric cancer (GC), a leading cause of global cancer mortality.ResultsWe performed full-length transcriptome analysis across 10 GC cell lines covering four major GC molecular subtypes (chromosomal unstable, Epstein-Barr positive, genome stable and microsatellite unstable). We identify 60,239 non-redundant full-length transcripts, of which > 66% are novel compared to current transcriptome databases. Novel isoforms are more likely to be cell line and subtype specific, expressed at lower levels with larger number of exons, with longer isoform/coding sequence lengths. Most novel isoforms utilize an alternate first exon, and compared to other alternative splicing categories, are expressed at higher levels and exhibit higher variability. Collectively, we observe alternate promoter usage in 25% of detected genes, with the majority (84.2%) of known/novel promoter pairs exhibiting potential changes in their coding sequences. Mapping these alternate promoters to TCGA GC samples, we identify several cancer-associated isoforms, including novel variants of oncogenes. Tumor-specific transcript isoforms tend to alter protein coding sequences to a larger extent than other isoforms. Analysis of outcome data suggests that novel isoforms may impart additional prognostic information.ConclusionsOur results provide a rich resource of full-length transcriptome data for deeper studies of GC and other gastrointestinal malignancies.

Long-read RNA sequencing of human and animal filarial parasites improves gene models and discovers operons.

Filarial parasitic nematodes (Filarioidea) cause substantial disease burden to humans and animals around the world. Recently there has been a coordinated global effort to generate, annotate, and curate genomic data from nematode species of medical and veterinary importance. This has resulted in two chromosome-level assemblies (Brugia malayi and Onchocerca volvulus) and 11 additional draft genomes from Filarioidea. These reference assemblies facilitate comparative genomics to explore basic helminth biology and prioritize new drug and vaccine targets. While the continual improvement of genome contiguity and completeness advances these goals, experimental functional annotation of genes is often hindered by poor gene models. Short-read RNA sequencing data and expressed sequence tags, in cooperation with ab initio prediction algorithms, are employed for gene prediction, but these can result in missing clade-specific genes, fragmented models, imperfect mapping of gene ends, and lack of isoform resolution. Long-read RNA sequencing can overcome these drawbacks and greatly improve gene model quality. Here, we present Iso-Seq data for B. malayi and Dirofilaria immitis, etiological agents of lymphatic filariasis and canine heartworm disease, respectively. These data cover approximately half of the known coding genomes and substantially improve gene models by extending untranslated regions, cataloging novel splice junctions from novel isoforms, and correcting mispredicted junctions. Furthermore, we validated computationally predicted operons, manually curated new operons, and merged fragmented gene models. We carried out analyses of poly(A) tails in both species, leading to the identification of non-canonical poly(A) signals. Finally, we prioritized and assessed known and putative anthelmintic targets, correcting or validating gene models for molecular cloning and target-based anthelmintic screening efforts. Overall, these data significantly improve the catalog of gene models for two important parasites, and they demonstrate how long-read RNA sequencing should be prioritized for ongoing improvement of parasitic nematode genome assemblies.

HLA Typing from Short-Read Sequencing Data with OptiType.

The established standards for HLA genotyping rely on targeted DNA sequencing techniques. However, the increasing abundance of short-read sequencing data has prompted a demand for computational tools capable of HLA typing from general purpose sequencing data as well. OptiType is a software that performs HLA typing from short-read DNA and RNA sequencing data, and this chapter guides the user through its installation and usage.

Transcriptome analyses provide insights into the difference of alkaloids biosynthesis in the Chinese goldthread (Coptis chinensis Franch.) from different biotopes.

Coptis chinensis Franch., the Chinese goldthread (‘Weilian’ in Chinese), one of the most important medicinal plants from the family Ranunculaceae, and its rhizome has been widely used in Traditional Chinese Medicine for centuries. Here, we analyzed the chemical components and the transcriptome of the Chinese goldthread from three biotopes, including Zhenping, Zunyi and Shizhu. We built comprehensive, high-quality de novo transcriptome assemblies of the Chinese goldthread from short-read RNA-Sequencing data, obtaining 155,710 transcripts and 56,071 unigenes. More than 98.39% and 95.97% of core eukaryotic genes were found in the transcripts and unigenes respectively, indicating that this unigene set capture the majority of the coding genes. A total of 520,462, 493,718, and 507,247 heterozygous SNPs were identified in the three accessions from Zhenping, Zunyi, and Shizhu respectively, indicating high polymorphism in coding regions of the Chinese goldthread (∼1%). Chemical analyses of the rhizome identified six major components, including berberine, palmatine, coptisine, epiberberine, columbamine, and jatrorrhizine. Berberine has the highest concentrations, followed by coptisine, palmatine, and epiberberine sequentially for all the three accessions. The drug quality of the accession from Shizhu may be the highest among these accessions. Differential analyses of the transcriptome identified four pivotal candidate enzymes, including aspartate aminotransferaseprotein, polyphenol oxidase, primary-amine oxidase, and tyrosine decarboxylase, were significantly differentially expressed and may be responsible for the difference of alkaloids contents in the accessions from different biotopes.

Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V'DJer.

Motivation: B-cell receptor (BCR) repertoire profiling is an important tool for understanding the biology of diverse immunologic processes. Current methods for analyzing adaptive immune receptor repertoires depend upon PCR amplification of VDJ rearrangements followed by long read amplicon sequencing spanning the VDJ junctions. While this approach has proven to be effective, it is frequently not feasible due to cost or limited sample material. Additionally, there are many existing datasets where short-read RNA sequencing data are available but PCR amplified BCR data are not.Results: We present here V’DJer, an assembly-based method that reconstructs adaptive immune receptor repertoires from short-read RNA sequencing data. This method captures expressed BCR loci from a standard RNA-seq assay. We applied this method to 473 Melanoma samples from The Cancer Genome Atlas and demonstrate V’DJer’s ability to accurately reconstruct BCR repertoires from short read mRNA-seq data.Availability and Implementation: V’DJer is implemented in C/C ++, freely available for academic use and can be downloaded from Github: https://github.com/mozack/vdjerContact: benjamin_vincent@med.unc.edu or parkerjs@email.unc.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Abstract 5120: The novel claudin-low molecular subtype of high-grade urothelial bladder cancer is highly immunogenic yet immunosuppressed

Abstract Rationale: High-grade urothelial carcinoma of the bladder is a heterogeneous disease, with molecular subtypes characterized by distinct tumor biologies and prognoses. Our group and others have described the basal and luminal subtypes, and we now report on the discovery of the claudin-low subtype, all of which resemble analogous subtypes in breast cancer. Methods: We analyzed mRNA sequencing and whole exome sequencing data for The Cancer Genome Atlas (TCGA) bladder tumors and tumors collected at UNC. We performed unsupervised hierarchical clustering on relative gene expression values with significance testing using SigClust to identify the claudin-low subtype. Basal, luminal, and claudin-low bladder tumors were compared for enrichment of genetic features (single nucleotide and copy number variation), gene set expression, immune gene signature expression, T cell receptor (TCR) and B cell receptor (BCR) gene segment expression, and number of predicted MHC Class I and Class II neoantigens. We further report on the first use of our VDJician software to reconstruct full-length rearranged BCR sequences from short-read RNA sequencing data in bladder cancer. Results: Claudin-low bladder tumors were defined by low expression of tight junction claudin-proteins, high expression of epithelial-to-mesenchymal transition genes and immune gene signatures, and were associated with reduced overall survival compared to luminal tumors. A minimal gene set classifier to identify claudin-low tumors showed some but not complete overlap with an optimal classifier derived in breast cancer. Claudin-low bladder tumors were enriched for multiple genetic features: increased rates of RB1, EP300, and NCOR1 mutations, increased frequency of EGFR amplification, decreased rates of FGFR3, ELF3, and KDM6A mutations, and decreased frequency of PPARG amplification. Claudin-low tumors showed the highest expression of immune gene signatures (including an immunosuppression signature), however in contrast to basal tumors, increased immune gene signature expression was not associated with prolonged survival. Claudin-low tumors also showed the highest overall expression of rearranged BCR sequences but lowest BCR repertoire diversity. Predicted neoantigen burden did not vary significantly by subtype, however broad cytokine and chemokine expression levels were elevated in claudin-low tumors, potentially related to low PPARG activity driving increased NFKB activity. Conclusions: Claudin-low bladder cancer is a novel molecular subtype with distinct molecular and immunologic features and prognostic significance. Given the presence of dense immune infiltrates, BCR repertoire characteristics consistent with an antigen-driven response, and high expression of immunosuppression genes, claudin-low bladder tumors may be primed to respond to immune checkpoint inhibitor therapy. Citation Format: Benjamin G. Vincent, William Kim, Jordan Kardos, Shengjie Chai, Joel Parker, Lisle Mose, Sara Selitsky, Michael Iglesia, Matthew Milowsky. The novel claudin-low molecular subtype of high-grade urothelial bladder cancer is highly immunogenic yet immunosuppressed. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5120.

Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V'DJer

Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V'DJer

Genome-guided transcript assembly by integrative analysis of RNA sequence data.

The identification of full length transcripts entirely from short-read RNA sequencing data (RNA-seq) remains a challenge in genome annotation pipelines. Here we describe an automated pipeline for genome annotation that integrates RNA-seq and gene-boundary data sets, which we call generalized RNA integration tool, or GRIT. By applying GRIT to Drosophila melanogaster short-read RNA-seq, cap analysis of gene expression (CAGE) and poly(A)-site-seq data collected for the modENCODE project, we recover the vast majority of previously annotated transcripts and double the total number of transcripts cataloged. We find that 20% of protein coding genes encode multiple protein-localization signals, and that, in 20 day old adult fly heads, genes with multiple poly-adenylation sites are more common than genes with alternate splicing or alternate promoters. When compared to the most widely used transcript assembly tools, GRIT recovers a larger fraction of annotated transcripts at higher precision. GRIT will enable the automated generation of high-quality genome annotations without necessitating extensive manual annotation.