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September 22, 2019  |  

Bayesian nonparametric discovery of isoforms and individual specific quantification.

Most human protein-coding genes can be transcribed into multiple distinct mRNA isoforms. These alternative splicing patterns encourage molecular diversity, and dysregulation of isoform expression plays an important role in disease etiology. However, isoforms are difficult to characterize from short-read RNA-seq data because they share identical subsequences and occur in different frequencies across tissues and samples. Here, we develop BIISQ, a Bayesian nonparametric model for isoform discovery and individual specific quantification from short-read RNA-seq data. BIISQ does not require isoform reference sequences but instead estimates an isoform catalog shared across samples. We use stochastic variational inference for efficient posterior estimates and demonstrate superior precision and recall for simulations compared to state-of-the-art isoform reconstruction methods. BIISQ shows the most gains for low abundance isoforms, with 36% more isoforms correctly inferred at low coverage versus a multi-sample method and 170% more versus single-sample methods. We estimate isoforms in the GEUVADIS RNA-seq data and validate inferred isoforms by associating genetic variants with isoform ratios.

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September 22, 2019  |  

Computational identification of novel genes: current and future perspectives.

While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies.

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September 22, 2019  |  

Rodent papillomaviruses.

Preclinical infection model systems are extremely valuable tools to aid in our understanding of Human Papillomavirus (HPV) biology, disease progression, prevention, and treatments. In this context, rodent papillomaviruses and their respective infection models are useful tools but remain underutilized resources in the field of papillomavirus biology. Two rodent papillomaviruses, MnPV1, which infects the Mastomys species of multimammate rats, and MmuPV1, which infects laboratory mice, are currently the most studied rodent PVs. Both of these viruses cause malignancy in the skin and can provide attractive infection models to study the lesser understood cutaneous papillomaviruses that have been frequently associated with HPV-related skin cancers. Of these, MmuPV1 is the first reported rodent papillomavirus that can naturally infect the laboratory strain of mice. MmuPV1 is an attractive model virus to study papillomavirus pathogenesis because of the ubiquitous availability of lab mice and the fact that this mouse species is genetically modifiable. In this review, we have summarized the knowledge we have gained about PV biology from the study of rodent papillomaviruses and point out the remaining gaps that can provide new research opportunities.

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September 22, 2019  |  

Revertant mosaicism repairs skin lesions in a patient with keratitis-ichthyosis-deafness syndrome by second-site mutations in connexin 26.

Revertant mosaicism (RM) is a naturally occurring phenomenon where the pathogenic effect of a germline mutation is corrected by a second somatic event. Development of healthy-looking skin due to RM has been observed in patients with various inherited skin disorders, but not in connexin-related disease. We aimed to clarify the underlying molecular mechanisms of suspected RM in the skin of a patient with keratitis-ichthyosis-deafness (KID) syndrome. The patient was diagnosed with KID syndrome due to characteristic skin lesions, hearing deficiency and keratitis. Investigation of GJB2 encoding connexin (Cx) 26 revealed heterozygosity for the recurrent de novo germline mutation, c.148G?>?A, p.Asp50Asn. At age 20, the patient developed spots of healthy-looking skin that grew in size and number within widespread erythrokeratodermic lesions. Ultra-deep sequencing of two healthy-looking skin biopsies identified five somatic nonsynonymous mutations, independently present in cis with the p.Asp50Asn mutation. Functional studies of Cx26 in HeLa cells revealed co-expression of Cx26-Asp50Asn and wild-type Cx26 in gap junction channel plaques. However, Cx26-Asp50Asn with the second-site mutations identified in the patient displayed no formation of gap junction channel plaques. We argue that the second-site mutations independently inhibit Cx26-Asp50Asn expression in gap junction channels, reverting the dominant negative effect of the p.Asp50Asn mutation. To our knowledge, this is the first time RM has been reported to result in the development of healthy-looking skin in a patient with KID syndrome. © The Author 2017. Published by Oxford University Press.

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September 22, 2019  |  

Gene activity in primary T cells infected with HIV89.6: intron retention and induction of genomic repeats.

HIV infection has been reported to alter cellular gene activity, but published studies have commonly assayed transformed cell lines and lab-adapted HIV strains, yielding inconsistent results. Here we carried out a deep RNA-Seq analysis of primary human T cells infected with the low passage HIV isolate HIV89.6.Seventeen percent of cellular genes showed altered activity 48 h after infection. In a meta-analysis including four other studies, our data differed from studies of HIV infection in cell lines but showed more parallels with infections of primary cells. We found a global trend toward retention of introns after infection, suggestive of a novel cellular response to infection. HIV89.6 infection was also associated with activation of several human endogenous retroviruses (HERVs) and retrotransposons, of interest as possible novel antigens that could serve as vaccine targets. The most highly activated group of HERVs was a subset of the ERV-9. Analysis showed that activation was associated with a particular variant of ERV-9 long terminal repeats that contains an indel near the U3-R border. These data also allowed quantification of >70 splice forms of the HIV89.6 RNA and specified the main types of chimeric HIV89.6-host RNAs. Comparison to over 100,000 integration site sequences from the same infected cell populations allowed quantification of authentic versus artifactual chimeric reads, showing that 5′ read-in, splicing out of HIV89.6 from the D4 donor and 3′ read-through were the most common HIV89.6-host cell chimeric RNA forms.Analysis of RNA abundance after infection of primary T cells with the low passage HIV89.6 isolate disclosed multiple novel features of HIV-host interactions, notably intron retention and induction of transcription of retrotransposons and endogenous retroviruses.

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September 22, 2019  |  

Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis.

RNA-sequencing (RNA-seq) is an essential technique for transcriptome studies, hundreds of analysis tools have been developed since it was debuted. Although recent efforts have attempted to assess the latest available tools, they have not evaluated the analysis workflows comprehensively to unleash the power within RNA-seq. Here we conduct an extensive study analysing a broad spectrum of RNA-seq workflows. Surpassing the expression analysis scope, our work also includes assessment of RNA variant-calling, RNA editing and RNA fusion detection techniques. Specifically, we examine both short- and long-read RNA-seq technologies, 39 analysis tools resulting in ~120 combinations, and ~490 analyses involving 15 samples with a variety of germline, cancer and stem cell data sets. We report the performance and propose a comprehensive RNA-seq analysis protocol, named RNACocktail, along with a computational pipeline achieving high accuracy. Validation on different samples reveals that our proposed protocol could help researchers extract more biologically relevant predictions by broad analysis of the transcriptome.RNA-seq is widely used for transcriptome analysis. Here, the authors analyse a wide spectrum of RNA-seq workflows and present a comprehensive analysis protocol named RNACocktail as well as a computational pipeline leveraging the widely used tools for accurate RNA-seq analysis.

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September 22, 2019  |  

A high-quality annotated transcriptome of swine peripheral blood.

High throughput gene expression profiling assays of peripheral blood are widely used in biomedicine, as well as in animal genetics and physiology research. Accurate, comprehensive, and precise interpretation of such high throughput assays relies on well-characterized reference genomes and/or transcriptomes. However, neither the reference genome nor the peripheral blood transcriptome of the pig have been sufficiently assembled and annotated to support such profiling assays in this emerging biomedical model organism. We aimed to assemble published and novel RNA-seq data to provide a comprehensive, well-annotated blood transcriptome for pigs by integrating a de novo assembly with a genome-guided assembly.A de novo and a genome-guided transcriptome of porcine whole peripheral blood was assembled with ~162 million pairs of paired-end and ~183 million single-end, trimmed and normalized Illumina RNA-seq reads (~6 billion initial reads from 146 RNA-seq libraries) from five independent studies by using the Trinity and Cufflinks software, respectively. We then removed putative transcripts (PTs) of low confidence from both assemblies and merged the remaining PTs into an integrated transcriptome consisting of 132,928 PTs, with 126,225 (~95%) PTs from the de novo assembly and more than 91% of PTs spliced. In the integrated transcriptome, ~90% and 63% of PTs had significant sequence similarity to sequences in the NCBI NT and NR databases, respectively; 68,754 (~52%) PTs were annotated with 15,965 unique gene ontology (GO) terms; and 7618 PTs annotated with Enzyme Commission codes were assigned to 134 pathways curated by the Kyoto Encyclopedia of Genes and Genomes (KEGG). Full exon-intron junctions of 17,528 PTs were validated by PacBio IsoSeq full-length cDNA reads from 3 other porcine tissues, NCBI pig RefSeq mRNAs and transcripts from Ensembl Sscrofa10.2 annotation. Completeness of the 5′ termini of 37,569 PTs was validated by public cap analysis of gene expression (CAGE) data. By comparison to the Ensembl transcripts, we found that (1) the deduced precursors of 54,402 PTs shared at least one intron or exon with those of 18,437 Ensembl transcripts; (2) 12,262 PTs had both longer 5′ and 3′ termini than their maximally overlapping Ensembl transcripts; and (3) 41,838 spliced PTs were totally missing from the Sscrofa10.2 annotation. Similar results were obtained when the PTs were compared to the pig NCBI RefSeq mRNA collection.We built, validated and annotated a comprehensive porcine blood transcriptome with significant improvement over the annotation of Ensembl Sscrofa10.2 and the pig NCBI RefSeq mRNAs, and laid a foundation for blood-based high throughput transcriptomic assays in pigs and for advancing annotation of the pig genome.

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September 22, 2019  |  

A comprehensive analysis of alternative splicing in paleopolyploid maize.

Identifying and characterizing alternative splicing (AS) enables our understanding of the biological role of transcript isoform diversity. This study describes the use of publicly available RNA-Seq data to identify and characterize the global diversity of AS isoforms in maize using the inbred lines B73 and Mo17, and a related species, sorghum. Identification and characterization of AS within maize tissues revealed that genes expressed in seed exhibit the largest differential AS relative to other tissues examined. Additionally, differences in AS between the two genotypes B73 and Mo17 are greatest within genes expressed in seed. We demonstrate that changes in the level of alternatively spliced transcripts (intron retention and exon skipping) do not solely reflect differences in total transcript abundance, and we present evidence that intron retention may act to fine-tune gene expression across seed development stages. Furthermore, we have identified temperature sensitive AS in maize and demonstrate that drought-induced changes in AS involve distinct sets of genes in reproductive and vegetative tissues. Examining our identified AS isoforms within B73 × Mo17 recombinant inbred lines (RILs) identified splicing QTL (sQTL). The 43.3% of cis-sQTL regulated junctions are actually identified as alternatively spliced junctions in our analysis, while 10 Mb windows on each side of 48.2% of trans-sQTLs overlap with splicing related genes. Using sorghum as an out-group enabled direct examination of loss or conservation of AS between homeologous genes representing the two subgenomes of maize. We identify several instances where AS isoforms that are conserved between one maize homeolog and its sorghum ortholog are absent from the second maize homeolog, suggesting that these AS isoforms may have been lost after the maize whole genome duplication event. This comprehensive analysis provides new insights into the complexity of AS in maize.

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September 22, 2019  |  

PLEK: a tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme.

High-throughput transcriptome sequencing (RNA-seq) technology promises to discover novel protein-coding and non-coding transcripts, particularly the identification of long non-coding RNAs (lncRNAs) from de novo sequencing data. This requires tools that are not restricted by prior gene annotations, genomic sequences and high-quality sequencing.We present an alignment-free tool called PLEK (predictor of long non-coding RNAs and messenger RNAs based on an improved k-mer scheme), which uses a computational pipeline based on an improved k-mer scheme and a support vector machine (SVM) algorithm to distinguish lncRNAs from messenger RNAs (mRNAs), in the absence of genomic sequences or annotations. The performance of PLEK was evaluated on well-annotated mRNA and lncRNA transcripts. 10-fold cross-validation tests on human RefSeq mRNAs and GENCODE lncRNAs indicated that our tool could achieve accuracy of up to 95.6%. We demonstrated the utility of PLEK on transcripts from other vertebrates using the model built from human datasets. PLEK attained >90% accuracy on most of these datasets. PLEK also performed well using a simulated dataset and two real de novo assembled transcriptome datasets (sequenced by PacBio and 454 platforms) with relatively high indel sequencing errors. In addition, PLEK is approximately eightfold faster than a newly developed alignment-free tool, named Coding-Non-Coding Index (CNCI), and 244 times faster than the most popular alignment-based tool, Coding Potential Calculator (CPC), in a single-threading running manner.PLEK is an efficient alignment-free computational tool to distinguish lncRNAs from mRNAs in RNA-seq transcriptomes of species lacking reference genomes. PLEK is especially suitable for PacBio or 454 sequencing data and large-scale transcriptome data. Its open-source software can be freely downloaded from https://sourceforge.net/projects/plek/files/.

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September 22, 2019  |  

Cataloguing over-expressed genes in Epstein Barr Virus immortalized lymphoblastoid cell lines through consensus analysis of PacBio transcriptomes corroborates hypomethylation of chromosome 1

The ability of Epstein Barr Virus (EBV) to transform resting cell B-cells into immortalized lymphoblastoid cell lines (LCL) provides a continuous source of peripheral blood lymphocytes that are used to model conditions in which these lymphocytes play a key role. Here, the PacBio generated transcriptome of three LCLs from a parent-daughter trio (SRAid:SRP036136) provided by a previous study [1] were analyzed using a kmer-based version of YeATS (KEATS). The set of over-expressed genes in these cell lines were determined based on a comparison with the PacBio transcriptome of twenty tissues pro- vided by another study (hOPTRS) [2]. MIR155 long non-coding RNA (MIR155HG), Fc fragment of IgE receptor II (FCER2), T-cell leukemia/lymphoma 1A (TCL1A), and germinal center associated signaling and motility (GCSAM) were genes having the highest expression counts in the three LCLs with no expression in hOPTRS. Other over-expressed genes, having low expression in hOPTRS, were membrane spanning 4-domains A1 (MS4A1) and ribosomal protein S2 pseudogene 55 (RPS2P55). While some of these genes are known to be over-expressed in LCLs, this study provides a comprehensive cataloguing of such genes. A recent work involving a patient with EBV-positive large B-cell lymphoma was “unusually lacking various B-cell markers”, but over-expressing CD30 [3] – a gene ranked 79 among uniquely expressed genes here. Hypomethylation of chromosome 1 observed in EBV immortalized LCLs [4, 5] is also corroborated here by mapping the genes to chromosomes. Extending previous work identifying un-annotated genes [6], 80 genes were identified which are expressed in the three LCLs, not in hOPTRS, and missing in the GENCODE, RefSeq and RefSeqGene databases. KEATS introduces a method of determining expression counts based on a partitioning of the known annotated genes, has runtimes of a few hours on a personal workstation and provides detailed reports enabling proper debugging.

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September 22, 2019  |  

Introduction to isoform sequencing using Pacific Biosciences technology (Iso-Seq)

Alternative RNA splicing is a known phenomenon, but we still do not have a complete catalog of isoforms that explain variability in the human transcriptome. We have made significant progress in developing methods to study variability of the transcriptome, but we are far away of having a complete picture of the transcriptome. The initial methods to study gene expression were based on cloning of cDNAs and Sanger sequencing. The strategy was labor-intensive and expensive. With the development of microarrays, different methods based on exon arrays and tiling arrays provided valuable information about RNA expression. However, the microarray presented significant limitations. Most of the limitations became apparent by 2005, but it was not until 2008 that an alternative method to study the transcriptome was developed. RNA Sequencing using next-generation sequencing (RNA-Seq) quickly became the technology of choice for gene expression profiling. Recently, the precision and sensitivity of RNA-Seq have come into question, especially for transcriptome reconstruction. This chapter will describe a relatively new method, “Isoform Sequencing (Iso-Seq). Iso-Seq was developed by Pacific Biosciences (PacBio), and it is capable of identifying new isoforms with extraordinary precision due to its long-read technology. The technique to create libraries is straightforward, and the PacBio RS II instrument generates the information in hours. The bioinformatics analysis is performed using the freely available SMRT® Portal software. The SMRT Portal is easy to use and capable of performing all the steps necessary to analyze the raw data and to generate high-quality full-length isoforms. For the universal acceptance of the Iso-Seq method, the capacity of the SMRT Cells needs to improve at least 10- to 100-fold to make the system affordable and attractive to users.

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September 22, 2019  |  

The new world of isoform sequencing

Not too long ago, the life sciences community was still debating whether sequencers would ever overtake microarrays as the preferred means of measuring gene expression. Today, not only have sequencers become the standard workhorse for gene expression studies, but newer sequencing technology has delivered the ability to generate novel expression data even in the most well-characterized cells or organisms. Truly, it is a remarkable time for comprehensive studies of which genes are being transcribed, with the goal of providing functional insight into various biological processes. The key advantage sequencing holds over microarrays is its ability to deeply survey an entire transcriptome, while microarrays are limited to interrogating known genes using probes designed from a reference genome assembly. As next-generation sequencing became more affordable, scientists were eager to switch to this approach, which became known as RNA sequencing or simply RNA-seq. © Mary Ann Liebert, Inc.

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September 22, 2019  |  

G&T-seq: parallel sequencing of single-cell genomes and transcriptomes.

The simultaneous sequencing of a single cell’s genome and transcriptome offers a powerful means to dissect genetic variation and its effect on gene expression. Here we describe G&T-seq, a method for separating and sequencing genomic DNA and full-length mRNA from single cells. By applying G&T-seq to over 220 single cells from mice and humans, we discovered cellular properties that could not be inferred from DNA or RNA sequencing alone.

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September 22, 2019  |  

SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification.

High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in well-annotated mammalian species. The advances in sequencing technology have created a need for studies and tools that can characterize these novel variants. Here, we present SQANTI, an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline using 47 unique descriptors. We apply SQANTI to a neuronal mouse transcriptome using Pacific Biosciences (PacBio) long reads and illustrate how the tool is effective in characterizing and describing the composition of the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, resulting more frequently in novel ORFs than novel UTRs, and are enriched in both general metabolic and neural-specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read-based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases, we find that alternative isoforms are elusive to proteogenomics detection. SQANTI allows the user to maximize the analytical outcome of long-read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes.© 2018 Tardaguila et al.; Published by Cold Spring Harbor Laboratory Press.

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September 22, 2019  |  

High-resolution comparative analysis of great ape genomes.

Genetic studies of human evolution require high-quality contiguous ape genome assemblies that are not guided by the human reference. We coupled long-read sequence assembly and full-length complementary DNA sequencing with a multiplatform scaffolding approach to produce ab initio chimpanzee and orangutan genome assemblies. By comparing these with two long-read de novo human genome assemblies and a gorilla genome assembly, we characterized lineage-specific and shared great ape genetic variation ranging from single- to mega-base pair-sized variants. We identified ~17,000 fixed human-specific structural variants identifying genic and putative regulatory changes that have emerged in humans since divergence from nonhuman apes. Interestingly, these variants are enriched near genes that are down-regulated in human compared to chimpanzee cerebral organoids, particularly in cells analogous to radial glial neural progenitors. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

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