High-throughput RNA sequencing: a step forward in transcriptome analysis

We created the PDX Network (PDXNet) Portal to provide an intuitive way for researchers to explore and understand the models, sequencing data, and bioinformatics workflows generated by NCI's PDXNet consortium for research access (https://portal.pdxnetwork.org/). The portal also provides metrics for PDXNet's activities, data processing protocols, and training materials for processing PDX data. The PDXNet Portal highlights model and data resources that include 216 new models across 29 cancer types. The most prevalent cancers represented in the PDX model dataset include invasive breast carcinoma (30.6%), melanoma (18.1%), and adenocarcinoma (14.4%). PDXNet teams have provided 2263 sequencing files from 356 samples across 204 patients, comprising whole exome (82.9%) and RNA seq files (17.1%). The most prevalent cancers represented in the PDXNet sequencing data set include Breast Pleural Effusion (27.2%), Breast Poorly Differentiated (12.5%), and Lung Adenocarcinoma (9.6%). The portal also provides access to 9492 sequencing files across 78 disease types that include 2594 samples across 463 patients uploaded from the NCI Patient-Derived Model Repository. The dataset includes both whole exomes (52.8%) and RNA seq (47.2%) data. The PDMR samples include PDX (82.7%), primary tumor (5.7%), normal germline (5.5), organoid culture (3.2), and Mixed Tumor Culture (2.9). The PDMR dataset also has multiple passages: P0 (21.8%), P1(39.5%), P2 (25.6%), and P3 (8.5%). These models and data resources support ten PDXNet Pilot activities, multiple publications, and international collaborations. PDXNet has also developed a set of 13 robust, validated, and standardized workflows for processing PDXNet whole-exome and RNA seq data. Collectively, these workflows allow for the standardized processing of PDX and complementary human tissues (normal and tumor). Our plan is to continuously update the model and data lists on the PDX portal as resources are generated. We expect that the PDXNet generated models, scheduled to grow to 1000 new models by 2022, will support multi-agent treatment studies, determination of mechanisms of sensitivity and resistance, and pre-clinical trials for example through the COMBO-MATCH program. The robust standard workflows currently processing all PDX sequencing data may also facilitate harmonizing data across studies. Lastly, we expect that the generated sequencing data will support computational approaches for studying cancer evolution and the mechanisms underlying cancer treatments. Citation Format: Soner Koc, Mike Lloyd, Steven Neuhauser, Javad Noodbakhsh, Anuj Srivastava, Xing Yi Woo, Ryan Jeon, Jeffrey Grover, Sara Seepo, Christian Frech, Jack DiGiovanna, PDXNet Consortium, Yvonne A. Evard, Tiffany Wallace, Jeffrey Moscow, James H. Doroshow, Nicholas Mitsuade, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis Carvarjal-Carmona, Alana Welm, Bryan Welm, Michael T. Lewis, Govindan Ramaswamy, Li Ding, Shunquang Li, Meenherd Herlyn, Mike Davies, Jack Roth, Funda Meric-Bernstam, Peter Robinson, Carol J. Bult, Brandi Davis-Dusenbery, Dennis A. Dean, Jeffrey H. Chuang. Advancing PDX research through model, data, and bioinformatics with the PDXNet Portal [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3017.

High-throughput sequencing reveals differing immune responses in the intestinal mucosa of two inbred lines afflicted with necrotic enteritis

High-throughput sequencing (HTS) refers to the simultaneous sequencing of millions of fragments of DNA, which can be either assembled to reconstitute a genome, or aligned to an existing reference genome. The protocol can be extended to assay a wide variety of biological states of the cell, including DNA copy number, mRNA abundance and various properties of chromatin. HTS experiments allow for these biological states to be quantified as read counts at genome-wide scale with a single experiment. Though the experiments are expensive and often datasets are produced with limited sample size, information can be shared across thousands of genomic ranges in order to obtain robust models which control for technical biases. In this thesis, I present three statistical models for analyzing HTS read count data, aimed at answering concise biological questions. First, a hidden Markov model is developed for detecting copy number variants (CNVs) in individual samples while controlling for technical artifacts, such as variation in read counts due to local GC-content. Applied to a study of 248 male patients with X-linked intellectual disability, the model predicts 16 large CNVs, of which 10 candidate disease-causing CNVs were tested and all experimentally validated. The proposed software is then compared with state-of-the-art segmentation algorithms on normalized data, showing higher sensitivity while controlling the total rate of predicted CNVs. Second, improvements for parameter estimation are made for a statistical model of differential gene expression from RNA-Seq data. The improvements involve the use of empirical Bayes priors – priors estimated using the observations from all genes – in order to moderate otherwise noisy estimates of dispersion and fold changes for individual genes. The improved model shows increased sensitivity and more robust estimation of fold change in comparison with other differential expression software packages for RNA-Seq. Finally, a hierarchical Bayes model is used to associate transcription factor binding with chromatin and sequence features in regions of accessible chromatin. The hierarchical model incorporates three levels of parameters: one for individual experiments, one for experiments of the same cell type and one across all cell types. The model parameters are used to generate hypotheses regarding the DNA-binding behavior of a transcription factor, the glucocorticoid receptor. In summary, this thesis describes a set of statistical methods for HTS read count data which can be used across various biological domains. The methods form a framework for robust estimation of variables and hypothesis testing.%%%%Mit Hochdurchsatz-Sequenzierverfahren (HTS) bezeichnet man das gleichzeitige Sequenzieren von Millionen von DNA-Fragmenten, welche entweder zur Genomrekonstrution genutzt oder auf ein bestehendes Referenzgenom aligniert werden konnen. Das Protokoll kann erweitert werden, um verschiedene biologische Zustande der Zelle, wie z.B. die Anzahl an DNA-Kopien, mRNA-Abundanzen oder…

Salinity and drought stress are the primary cause of crop losses worldwide. In sodic saline soilssodium chloride (NaCl) disrupts normal plant growth and development. The complex interactions ofplant systems with abiotic stress have made RNA sequencing a more holistic and appealing approach tostudy transcriptome level responses in a single cell and/or tissue. In this work, we determined thePetunia transcriptome response to NaCl stress by sequencing leaf samples andassembling 196 million Illumina reads with Trinity software. Using our reference transcriptome weidentified more than 7,000 genes that were differentially expressed within 24 h of acute NaClstress. The proposed transcriptome can also be used as an excellent tool for biological andbioinformatics in the absence of an available Petunia genome and it is available atthe SOL Genomics Network (SGN) http://solgenomics.net. Genes related to regulation of reactive oxygen species,transport, and signal transductions as well as novel and undescribed transcripts were among thosedifferentially expressed in response to salt stress. The candidate genes identified in this studycan be applied as markers for breeding or to genetically engineer plants to enhance salt tolerance.Gene Ontology analyses indicated that most of the NaCl damage happened at 24 h inducinggenotoxicity, affecting transport and organelles due to the high concentration of Na+ions. Finally, we report a modification to the library preparation protocol whereby cDNA sampleswere bar-coded with non-HPLC purified primers, without affecting the quality and quantity of theRNA-seq data. The methodological improvement presented here could substantially reduce the cost ofsample preparation for future high-throughput RNA sequencing experiments.

Motivation: Understanding the occurrence and regulation of alternative splicing (AS) is a key task towards explaining the regulatory processes that shape the complex transcriptomes of higher eukaryotes. With the advent of high-throughput sequencing of RNA (RNA-Seq), the diversity of AS transcripts could be measured at an unprecedented depth. Although the catalog of known AS events has grown ever since, novel transcripts are commonly observed when working with less well annotated organisms, in the context of disease, or within large populations. Whereas an identification of complete transcripts is technically challenging and computationally expensive, focusing on single splicing events as a proxy for transcriptome characteristics is fruitful and sufficient for a wide range of analyses.Results: We present SplAdder, an alternative splicing toolbox, that takes RNA-Seq alignments and an annotation file as input to (i) augment the annotation based on RNA-Seq evidence, (ii) identify alternative splicing events present in the augmented annotation graph, (iii) quantify and confirm these events based on the RNA-Seq data and (iv) test for significant quantitative differences between samples. Thereby, our main focus lies on performance, accuracy and usability.Availability: Source code and documentation are available for download at http://github.com/ratschlab/spladder. Example data, introductory information and a small tutorial are accessible via http://bioweb.me/spladder.Contacts: andre.kahles@ratschlab.org or gunnar.ratsch@ratschlab.orgSupplementary information:Supplementary data are available at Bioinformatics online.

High-throughput sequencing of RNA is used to analyze the transcriptomes of viruses and cells, providing information about transcript structure and abundance. A wide array of programs and pipelines has been created to manage and interpret the abundance of data generated from high-throughput RNA sequencing experiments. This protocol details the use of free and open-source programs to align RNA-Seq reads to a reference genome, visualize read coverage and splice junctions, estimate transcript abundance, and evaluate differential expression of transcripts in different conditions. Particular concerns related to EBV and viral transcriptomics are addressed and access to EBV reference files is provided.

Using machine learning and high-throughput RNA sequencing to classify the precursors of small non-coding RNAs

High throughput RNA sequencing (RNA-seq) technology is increasingly used in disease-related biomarker studies. A negative binomial distribution has become the popular choice for modeling read counts of genes in RNA-seq data due to over-dispersed read counts. In this study, we propose two explicit sample size calculation methods for RNA-seq data using a negative binomial regression model. To derive these new sample size formulas, the common dispersion parameter and the size factor as an offset via a natural logarithm link function are incorporated. A two-sided Wald test statistic derived from the coefficient parameter is used for testing a single gene at a nominal significance level 0.05 and multiple genes at a false discovery rate 0.05. The variance for the Wald test is computed from the variance-covariance matrix with the parameters estimated from the maximum likelihood estimates under the unrestricted and constrained scenarios. The performance and a side-by-side comparison of our new formulas with three existing methods with a Wald test, a likelihood ratio test or an exact test are evaluated via simulation studies. Since other methods are much computationally extensive, we recommend our M1 method for quick and direct estimation of sample sizes in an experimental design. Finally, we illustrate sample sizes estimation using an existing breast cancer RNA-seq data.

The high throughput RNA sequencing (RNA-seq) technology has become the popular method of choice for transcriptomics and the detection of differentially expressed genes. Sample size calculations for RNA-seq experimental design are an important consideration in biological research and clinical trials. Currently, the sample size formulas derived from the Wald and the likelihood ratio statistical tests with a Poisson distribution to model RNA-seq data have been developed. However, since the mean read counts in the real RNA-seq data are not equal to the variance, an extended method to calculate sample sizes based on a negative binomial distribution using an exact test statistic was proposed by Li et al. in 2013. In this study, we alternatively derive five sample size calculation methods based on the negative binomial distribution using the Wald test, the log-transformed Wald test and the log-likelihood ratio test statistics. A comparison of our five methods and an existing method was performed by calculating the sample sizes and the simulated power in different scenarios. We first calculated the sample sizes for testing a single gene using the six methods given a nominal significance level α at 0.05 and 80% power. Then, we calculated the sample sizes for testing multiple genes given a false discovery rate (FDR) at 0.05 and 0.10. The empirical power and true prognostic genes for differential gene expression analysis corresponding to the estimated sample sizes from the six methods are also estimated via the simulation studies. Using the sample size formulas derived from log-transformed and Wald-based tests, we observed smaller sample properties while maintaining the nominal power close to or higher than 80% in all the settings compared to other methods. Moreover, the Wald test based sample size calculation method is easier to compute and faster in an RNA-seq experimental design.

Recent advances in high-throughput RNA sequencing (RNA-seq) have enabled tremendous leaps forward in our understanding of bacterial transcriptomes. However, computational methods for analysis of bacterial transcriptome data have not kept pace with the large and growing data sets generated by RNA-seq technology. Here, we present new algorithms, specific to bacterial gene structures and transcriptomes, for analysis of RNA-seq data. The algorithms are implemented in an open source software system called Rockhopper that supports various stages of bacterial RNA-seq data analysis, including aligning sequencing reads to a genome, constructing transcriptome maps, quantifying transcript abundance, testing for differential gene expression, determining operon structures and visualizing results. We demonstrate the performance of Rockhopper using 2.1 billion sequenced reads from 75 RNA-seq experiments conducted with Escherichia coli, Neisseria gonorrhoeae, Salmonella enterica, Streptococcus pyogenes and Xenorhabdus nematophila. We find that the transcriptome maps generated by our algorithms are highly accurate when compared with focused experimental data from E. coli and N. gonorrhoeae, and we validate our system’s ability to identify novel small RNAs, operons and transcription start sites. Our results suggest that Rockhopper can be used for efficient and accurate analysis of bacterial RNA-seq data, and that it can aid with elucidation of bacterial transcriptomes.

During embryonic development, proper vertebrate body patterning is achieved through a series of highly regulated transcriptional mechanisms that result in a precise spatial and temporal control of specific master genes. Hox transcription factors play a crucial role in the specification of posterior positional identity, acting as part of a downstream regulatory network responding to Retinoic Acid (RA) activity. RALDH2 enzyme is solely responsible for embryonic RA synthesis until E8.5, and its mutation affects dramatically the development of different structures and organs such as heart, somites, pharyngeal arches, limb and neural tube (Niederreither et al., 1999). Yet, little is known about the molecular mechanisms involved in its regulation. Previous literature showed that Pbx mutant mice phenocopy most of the defects exhibit by Raldh2–/– mutant animals (Capellini et al., 2006; Manley et al., 2004; Stankunas et al., 2008). Moreover, Pbx proteins are well characterized Hox cofactors (reviewed in Moens and Selleri, 2006). In the first part of this work we investigate the role of Hox and Pbx transcription factors in the maintenance of RALDH2 expression. Using genetic tools and biochemical assays such as in situ hybridization, reporter gene analysis of RA activity, chromatin immunoprecipitation (ChIP), electrophoretic mobility shift assay (EMSA) and BAC recombineering we address this important question. Furthermore, the generation of an early anterior boundary of RA activity, obtained through the complementary distribution of synthesizing and degrading enzymes, identifies a rostral embryonic domain devoid of Hox gene expression. Previously, it has been demonstrated that the maintenance of a Hox-negative domain is an essential condition required for the correct morphogenesis of vertebrate craniofacial structures (Couly et al., 1998; Creuzet et al., 2002). During early phases of neurogenesis, exogenous administration of RA or mutation of CYP degrading enzymes result in the anterior shift of Hox gene expression in the hindbrain and in the corresponding NCCs populating the pharyngeal regions (Hernandez et al., 2007; Mallo and Brandlin, 1997; Marshall et al., 1992; Mulder et al., 1998). These effects, associated with other RA-mediated molecular changes in the signalling epithelium of first pharyngeal arch, lead to impairment of craniofacial development (Mallo and Brandlin, 1997; Vieux-Rochas et al., 2007). Later on, anterior Hox genes become unresponsive to RA signalling and its exogenous administration no longer affects head and pharyngeal patterning. These evidences suggest a possible role of epigenetic silencing mechanisms in the maintenance of transcriptional repression of Hox gene in the face. Takihara and colleagues (Takihara et al., 1997) show that Phc1 disruption (the mouse homologue of the Drosophila polyhomeotic gene) leads to altered antero-posterior pattening and neural crest defects. Furthermore, Phc2 and Phc1 have been shown to act synergistically to establish a Polycomb-mediated repression of Hox genes (Isono et al, 2005). Although these works underscore the function PRC1 complex in the maintenance of transcriptional repression of Hox genes during antero-posterior specification, they do not account for the general function of Polycomb-mediated silencing in NCCs. Indeed, although a large majority of Polycomb targets are co-occupied by PRC2 and PCR1 complexes in ES cells, there is a substantial portion of target genes that show non-overlapping characteristics (Boyler et al., 2006; Ku et al., 2008). Moreover, a comprehensive analysis of craniofacial defects is missing, partly due to the early lethality of the analyzed mutant mice. In the second part of this work we address the genome-wide impact of cell-autonomous Ezh2 mutation during craniofacial development in the mouse and we discuss the implications of our results in the context of collinear expression of Hox genes and their chromatin architecture inside the nucleus. Using ChIP coupled with high throughput sequencing (ChIP-seq) and RNA-seq data, we identify the epigenomic and transcriptomic features of defined rostro-caudal cranial NCC populations. This study deciphers the role of PCR2 during head and pharyngeal morphogenesis.

The potential of mitochondrial DNA (mtDNA) genes are well-known for species identification and to establish a phylogenetic relationship. The De-novo transcriptome assembly of Acheta domesticus commonly known as house cricket, is provides important segments of DNA fragments from mitochondrial DNA due to higher abundance of its mRNA. When the reference sequence with gene annotation is absent for assembling and aligning desire gene sequences, like in the present case, the most similar sequence is obtained from online insect mitochondrial genome database to find mitochondrial DNA conserved domains of interested gene from high throughput RNA sequencing (RNA-seq) data. The RNA-seq data of Acheta domesticus transcriptome is used to retrieve single nucleotide fragment out of 50,046 assembled contigs to discover three important genes from mtDNA of the house cricket. Present study provides effective workflow to identify genes like cytochrome c oxidase subunit II (COX2), NADH dehydrogenase subunit 2 (ND2), cytochrome c oxidase subunit I (COX1) from mtDNA in large sequence archive of RNA-seq data. These three novel barcode sequences will be useful for genetic identification and evolution investigation of Acheta domesticus. The partial mtDNA sequence with these genes will be important for mitochondrial genome construction.

Introduction We developed a unique panel of 54 target genes dedicated to hematological malignancies (HematoPanel) and screened 1,108 clinical samples. We also performed transcriptomic analysis and evaluated its relevance for patients with acute lymphoblastic leukemia (ALL). Methods DNA and RNA were extracted from cell pellets with the Maxwell® RSC (Promega®). Libraries for targeted high-throughput DNA sequencing (HTS) were generated in duplicate from 100 ng with the Advanta™ NGS Library Prep Assay kit using the LP 48.48 IFC (integrated fluidic circuit) on a Juno™ and sequenced on the NextSeq™ 550 (Illumina®) to a median depth of coverage of approximately 1,200X per sample. Transcriptomic libraries were generated from 50 ng RNA with the Advanta RNA-Seq XT NGS Library Prep Kit using the LP 48.Atlas™ IFC on Juno and sequenced on the NextSeq 550. A bioinformatics pipeline was developed for fusion transcript detection. Differential gene expression was analyzed with the DESeq2 package after STAR alignment. Results In 2020 we sequenced 1,108 consecutive samples in routine practice with HematoPanel. The main indications were the suspicion of myeloproliferative neoplasms (32%), the evaluation of chronic lymphocytic leukemia (23%), and the diagnostic workup of cytopenias (15%). We detected 1,306 mutations in 654 patients (59%) (1 to 8 mutations per sample). Eight genes (TET2, SF3B1, TP53, DNMT3A, ASXL1, JAK2, SRSF2 and MYD88) were reported as mutated for more than 50 patients. We sequenced 22 samples including the MOLT-4 cell line, healthy donor samples, and 16 ALL samples for transcriptomic evaluation. From ALL samples, we detected 5 fusion transcripts previously identified by specific PCR (1 BCR-ABL, 2 ETV6-RUNX1, 1 KMT2A-AFF1, 1 TCF3-PBX1). Moreover, we detected 4 other fusion transcripts for 3 patients (EBF1-PDGFRB, FGFR1-MYO18A, P2RY8-CRLF2, and PAX5-NOL4L). Interestingly, these patients clustered with the BCR-ABL positive sample based on their expression profile. It confirms the diagnosis of Phi-like ALL and thus offers a therapeutic target for these patients. Conclusion Microfluidics-based library prep using Juno offers a cost-effective strategy and straightforward workflows for both targeted HTS and RNA sequencing. We validated its use for hematological malignancy evaluation in routine practice. Citation Format: Cédric Pastoret, Amyra Aliouat, Marie-Laure Boulland, Florent Denoual, Anne Desmares, Marie De Tayrac, Thierry Fest, Celeste Letellier. Microfluidics-based library preparation strategies for targeted high-throughput sequencing and RNA sequencing in hematological malignancies in clinical samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5791.

With the development of high-throughput techniques, RNA-sequencing (RNA-seq) is becoming increasingly popular as an alternative for gene expression analysis, such as RNAs profiling and classification. Identifying which type of diseases a new patient belongs to with RNA-seq data has been recognized as a vital problem in medical research. As RNA-seq data are discrete, statistical methods developed for classifying microarray data cannot be readily applied for RNA-seq data classification. Witten proposed a Poisson linear discriminant analysis (PLDA) to classify the RNA-seq data in 2011. Note, however, that the count datasets are frequently characterized by excess zeros in real RNA-seq or microRNA sequence data (i.e. when the sequence depth is not enough or small RNAs with the length of 18-30 nucleotides). Therefore, it is desired to develop a new model to analyze RNA-seq data with an excess of zeros. In this paper, we propose a Zero-Inflated Poisson Logistic Discriminant Analysis (ZIPLDA) for RNA-seq data with an excess of zeros. The new method assumes that the data are from a mixture of two distributions: one is a point mass at zero, and the other follows a Poisson distribution. We then consider a logistic relation between the probability of observing zeros and the mean of the genes and the sequencing depth in the model. Simulation studies show that the proposed method performs better than, or at least as well as, the existing methods in a wide range of settings. Two real datasets including a breast cancer RNA-seq dataset and a microRNA-seq dataset are also analyzed, and they coincide with the simulation results that our proposed method outperforms the existing competitors. The software is available at http://www.math.hkbu.edu.hk/∼tongt. xwan@comp.hkbu.edu.hk or tongt@hkbu.edu.hk. Supplementary data are available at Bioinformatics online.

Transcriptome sequencing (mRNA-seq) is becoming a very versatile technique for profiling tumors, extending beyond its original intent of transcript quantification, identification of alternative transcripts, and detection of gene fusions. For instance, through recent advancements in RNA-seq data analyses, one can now computationally assess allele-specific gene expression and generate profiles of expressed somatic mutations. Here, we demonstrate the ability to identify chromosomal allelic imbalances (AI) through detection of haplotype-specific patterns in gene transcripts. This class of RNA-based observations may potentially reveal DNA-level chromosomal allelic imbalances or uncover large regions of transcription deregulation. From the TCGA Uterine Carcinosarcoma project, we downloaded exome and RNA sequencing data for 48 patients’ tumor/normal sample pairs in addition to their clinical annotations. We also downloaded Affymetrix SNP6-based DNA copy number event calls made by the TCGA for use as a gold standard when evaluating the AI calls in the exome and RNA-seq. AI calls were made in both the exome and RNA-seq data by: (1) calling 1000 Genomes genotypes in the sequencing data, (2) phasing haplotypes and then (3) characterizing haplotype imbalances using a tool that we developed called hapLOHseq. hapLOHseq applied to the exome and RNA-seq data both resulted in a 72% specificity for identifying the gold standard AI events. In RNA-seq data we detected 43% of the chromosomal AI events identified in the exome sequencing data. When considering AI events specifically detected in the RNA-seq and not the gold standard (RNA-specific AI), the data suggest that higher RNA-specific AI loads could be negatively associated with survival (p-val = 0.076), with higher RNA-specific AI load patients having a median survival of 771 days compared to 1526 days for those patients with lower loads of RNA-specific AI. In conclusion, our results suggest that analysis of chromosomal AI in RNA-seq has equal specificity for detecting DNA-level AI when compared to exome sequencing, although at lower sensitivities. Clinically, our analyses suggest that patients with higher RNA-specific AI load may have a worse overall survival prognosis. The AI we are identifying in the RNA-seq samples may reflect large-scale transcription defects, resulting in a negative impact on the survival of patients. One possible cause of RNA-specific allelic imbalance could be the presence of cis mutations that impact a large-region of the transcription of one of the two haplotypes. Currently, we are identifying areas for improvement in our analytical methods, while interrogating and characterizing exome and RNA-seq AI in additional data sets. Citation Format: Francis A. San Lucas, Yihua Liu, Zachary Weber, Erik Ehli, Gareth Davies, Paul Scheet. Characterization of chromosomal allelic imbalances through RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3572. doi:10.1158/1538-7445.AM2017-3572