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Auto Tag: Human genome

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  |  

Lipoprotein lipase reaches the capillary lumen in chickens despite an apparent absence of GPIHBP1.

In mammals, GPIHBP1 is absolutely essential for transporting lipoprotein lipase (LPL) to the lumen of capillaries, where it hydrolyzes the triglycerides in triglyceride-rich lipoproteins. In all lower vertebrate species (e.g., birds, amphibians, reptiles, fish), a gene for LPL can be found easily, but a gene for GPIHBP1 has never been found. The obvious question is whether the LPL in lower vertebrates is able to reach the capillary lumen. Using purified antibodies against chicken LPL, we showed that LPL is present on capillary endothelial cells of chicken heart and adipose tissue, colocalizing with von Willebrand factor. When the antibodies against chicken LPL were injected intravenously into chickens, they bound to LPL on the luminal surface of capillaries in heart and adipose tissue. LPL was released rapidly from chicken hearts with an infusion of heparin, consistent with LPL being located inside blood vessels. Remarkably, chicken LPL bound in a specific fashion to mammalian GPIHBP1. However, we could not identify a gene for GPIHBP1 in the chicken genome, nor could we identify a transcript for GPIHBP1 in a large chicken RNA-seq data set. We conclude that LPL reaches the capillary lumen in chickens – as it does in mammals – despite an apparent absence of GPIHBP1.

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September 22, 2019  |  

Single Molecule Sequencing: new outlooks for solving genome assembly and transcripts identification challenges

In this review, we introduce a novel sequencing technology, named Single Molecule Real Time sequencing. Also called Single Molecule Sequencing, as it do not requires any amplification, this new technology is able to pro- duce much longer reads than previous NGS technologies such as Illumina. This read size improvements, which can reach 150 fold, will solve many challenges caused by the actual NGS technologies. Short NGS reads, reach- ing a maximum size of 300 bp, make it hard to reconstitute a whole genome and are always leading to fragmented genome assembly. It is also difficult to correctly infer transcript quantification and identification when there is a high isoforms diversity. Despite their higher error rate, long reads have shown very promising result concerning these actual issues. We show that longer reads can produce less fragmented assembly, with a better quality, but also sequence from start to end mRNA, making it much more easier to infer correct transcript quantification, and even allow new intron structure and so new isoforms discovery.

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September 22, 2019  |  

Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations.

We analyzed transcriptomes (n = 211), whole exomes (n = 99) and targeted exomes (n = 103) from 216 malignant pleural mesothelioma (MPM) tumors. Using RNA-seq data, we identified four distinct molecular subtypes: sarcomatoid, epithelioid, biphasic-epithelioid (biphasic-E) and biphasic-sarcomatoid (biphasic-S). Through exome analysis, we found BAP1, NF2, TP53, SETD2, DDX3X, ULK2, RYR2, CFAP45, SETDB1 and DDX51 to be significantly mutated (q-score = 0.8) in MPMs. We identified recurrent mutations in several genes, including SF3B1 (~2%; 4/216) and TRAF7 (~2%; 5/216). SF3B1-mutant samples showed a splicing profile distinct from that of wild-type tumors. TRAF7 alterations occurred primarily in the WD40 domain and were, except in one case, mutually exclusive with NF2 alterations. We found recurrent gene fusions and splice alterations to be frequent mechanisms for inactivation of NF2, BAP1 and SETD2. Through integrated analyses, we identified alterations in Hippo, mTOR, histone methylation, RNA helicase and p53 signaling pathways in MPMs.

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September 22, 2019  |  

ALE: a generic assembly likelihood evaluation framework for assessing the accuracy of genome and metagenome assemblies.

Researchers need general purpose methods for objectively evaluating the accuracy of single and metagenome assemblies and for automatically detecting any errors they may contain. Current methods do not fully meet this need because they require a reference, only consider one of the many aspects of assembly quality or lack statistical justification, and none are designed to evaluate metagenome assemblies.In this article, we present an Assembly Likelihood Evaluation (ALE) framework that overcomes these limitations, systematically evaluating the accuracy of an assembly in a reference-independent manner using rigorous statistical methods. This framework is comprehensive, and integrates read quality, mate pair orientation and insert length (for paired-end reads), sequencing coverage, read alignment and k-mer frequency. ALE pinpoints synthetic errors in both single and metagenomic assemblies, including single-base errors, insertions/deletions, genome rearrangements and chimeric assemblies presented in metagenomes. At the genome level with real-world data, ALE identifies three large misassemblies from the Spirochaeta smaragdinae finished genome, which were all independently validated by Pacific Biosciences sequencing. At the single-base level with Illumina data, ALE recovers 215 of 222 (97%) single nucleotide variants in a training set from a GC-rich Rhodobacter sphaeroides genome. Using real Pacific Biosciences data, ALE identifies 12 of 12 synthetic errors in a Lambda Phage genome, surpassing even Pacific Biosciences’ own variant caller, EviCons. In summary, the ALE framework provides a comprehensive, reference-independent and statistically rigorous measure of single genome and metagenome assembly accuracy, which can be used to identify misassemblies or to optimize the assembly process.ALE is released as open source software under the UoI/NCSA license at http://www.alescore.org. It is implemented in C and Python.

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September 22, 2019  |  

Capturing a long look at our genetic library.

Long-read sequencing, coupled to cDNA capture, provides an unrivaled view of the transcriptome of chromosome 21, revealing surprises about the splicing of long noncoding RNAs. Copyright © 2018. Published by Elsevier Inc.

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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  |  

JAFFA: High sensitivity transcriptome-focused fusion gene detection.

Genomic instability is a hallmark of cancer and, as such, structural alterations and fusion genes are common events in the cancer landscape. RNA sequencing (RNA-Seq) is a powerful method for profiling cancers, but current methods for identifying fusion genes are optimised for short reads. JAFFA (https://github.com/Oshlack/JAFFA/wiki) is a sensitive fusion detection method that outperforms other methods with reads of 100 bp or greater. JAFFA compares a cancer transcriptome to the reference transcriptome, rather than the genome, where the cancer transcriptome is inferred using long reads directly or by de novo assembling short reads.

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September 22, 2019  |  

Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line.

The SK-BR-3 cell line is one of the most important models for HER2+ breast cancers, which affect one in five breast cancer patients. SK-BR-3 is known to be highly rearranged, although much of the variation is in complex and repetitive regions that may be underreported. Addressing this, we sequenced SK-BR-3 using long-read single molecule sequencing from Pacific Biosciences and develop one of the most detailed maps of structural variations (SVs) in a cancer genome available, with nearly 20,000 variants present, most of which were missed by short-read sequencing. Surrounding the important ERBB2 oncogene (also known as HER2), we discover a complex sequence of nested duplications and translocations, suggesting a punctuated progression. Full-length transcriptome sequencing further revealed several novel gene fusions within the nested genomic variants. Combining long-read genome and transcriptome sequencing enables an in-depth analysis of how SVs disrupt the genome and sheds new light on the complex mechanisms involved in cancer genome evolution.© 2018 Nattestad et al.; Published by Cold Spring Harbor Laboratory Press.

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September 22, 2019  |  

Next-generation sequencing for pathogen detection and identification

Over the past decade, the field of genomics has seen such drastic improvements in sequencing chemistries that high-throughput sequencing, or next-generation sequencing (NGS), is being applied to generate data across many disciplines. NGS instruments are becoming less expensive, faster, and smaller, and therefore are being adopted in an increasing number of laboratories, including clinical laboratories. Thus far, clinical use of NGS has been mostly focused on the human genome, for purposes such as characterizing the molecular basis of cancer or for diagnosing and understanding the basis of rare genetic disorders. There are, however, an increasing number of examples whereby NGS is employed to discover novel pathogens, and these cases provide precedent for the use of NGS in microbial diagnostics. NGS has many advantages over traditional microbial diagnostic methods, such as unbiased rather than pathogen-specific protocols, ability to detect fastidious or non-culturable organisms, and ability to detect co-infections. One of the most impressive advantages of NGS is that it requires little or no prior knowledge of the pathogen, unlike many other diagnostic assays; therefore for pathogen discovery, NGS is very valuable. However, despite these advantages, there are challenges involved in implementing NGS for routine clinical microbiological diagnosis. We discuss these advantages and challenges in the context of recently described research studies.

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September 22, 2019  |  

Single molecule real-time (SMRT) sequencing comes of age: applications and utilities for medical diagnostics.

Short read massive parallel sequencing has emerged as a standard diagnostic tool in the medical setting. However, short read technologies have inherent limitations such as GC bias, difficulties mapping to repetitive elements, trouble discriminating paralogous sequences, and difficulties in phasing alleles. Long read single molecule sequencers resolve these obstacles. Moreover, they offer higher consensus accuracies and can detect epigenetic modifications from native DNA. The first commercially available long read single molecule platform was the RS system based on PacBio’s single molecule real-time (SMRT) sequencing technology, which has since evolved into their RSII and Sequel systems. Here we capsulize how SMRT sequencing is revolutionizing constitutional, reproductive, cancer, microbial and viral genetic testing.© The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.

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September 22, 2019  |  

Dual platform long-read RNA-sequencing dataset of the human Cytomegalovirus Lytic transcriptome

RNA-sequencing has revolutionized transcriptomics and the way we measure gene expression (Wang et al., 2009). As of today, short-read RNA sequencing is more widely used, and due to its low price and high throughput, is the preferred tool for the quantitative analysis of gene expression. However, the annotation of transcript isoforms is rather difficult using only short-read sequencing data, because the reads are shorter than most transcripts (Steijger et al., 2013). Long-read sequencing, on the other hand, can provide full contig information about transcripts, including exon-connectivity, and its merits in transcriptome profiling are being increasingly acknowledged (Sharon et al., 2013; Abdel-Ghany et al., 2016; Wang et al., 2016; Kuo et al., 2017). Due to the relatively low throughput of current long-read sequencing technologies, they can only characterize smaller transcriptomes in high-depth (Weirather et al., 2017). The Human cytomegalovirus (HCMV) is a ubiquitous betaherpesvirus, which can cause mononucleosis-like symptoms in adults (Cohen and Corey, 1985), and severe life-threatening infections in newborns (Wen et al., 2002). Latent HCMV infection has recently been implicated to affect cancer formation (Dziurzynski et al., 2012; Jin et al., 2014). Examining the transcriptome of the virus can go a long way in helping understand its molecular biology. Short-read RNA sequencing studies have discovered splice junctions and non-coding transcripts (Gatherer et al., 2011) and have shown that the most abundant HCMV transcripts are similarly expressed in different cell types (Cheng et al., 2017). Our long-read RNA sequencing experiments using the Pacific Biosciences (PacBio) RSII platform revealed a great number of transcript isoforms, polycistronic RNAs and transcriptional overlaps (Balázs et al., 2017a).

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September 22, 2019  |  

Metataxonomic and metagenomic approaches vs. culture-based techniques for clinical pathology.

Diagnoses that are both timely and accurate are critically important for patients with life-threatening or drug resistant infections. Technological improvements in High-Throughput Sequencing (HTS) have led to its use in pathogen detection and its application in clinical diagnoses of infectious diseases. The present study compares two HTS methods, 16S rRNA marker gene sequencing (metataxonomics) and whole metagenomic shotgun sequencing (metagenomics), in their respective abilities to match the same diagnosis as traditional culture methods (culture inference) for patients with ventilator associated pneumonia (VAP). The metagenomic analysis was able to produce the same diagnosis as culture methods at the species-level for five of the six samples, while the metataxonomic analysis was only able to produce results with the same species-level identification as culture for two of the six samples. These results indicate that metagenomic analyses have the accuracy needed for a clinical diagnostic tool, but full integration in diagnostic protocols is contingent on technological improvements to decrease turnaround time and lower costs.

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September 22, 2019  |  

Next-generation approaches to advancing eco-immunogenomic research in critically endangered primates.

High-throughput sequencing platforms are generating massive amounts of genomic data from nonmodel species, and these data sets are valuable resources that can be mined to advance a number of research areas. An example is the growing amount of transcriptome data that allow for examination of gene expression in nonmodel species. Here, we show how publicly available transcriptome data from nonmodel primates can be used to design novel research focused on immunogenomics. We mined transcriptome data from the world’s most endangered group of primates, the lemurs of Madagascar, for sequences corresponding to immunoglobulins. Our results confirmed homology between strepsirrhine and haplorrhine primate immunoglobulins and allowed for high-throughput sequencing of expressed antibodies (Ig-seq) in Coquerel’s sifaka (Propithecus coquereli). Using both Pacific Biosciences RS and Ion Torrent PGM sequencing, we performed Ig-seq on two individuals of Coquerel’s sifaka. We generated over 150 000 sequences of expressed antibodies, allowing for molecular characterization of the antigen-binding region. Our analyses suggest that similar VDJ expression patterns exist across all primates, with sequences closely related to the human VH 3 immunoglobulin family being heavily represented in sifaka antibodies. Moreover, the antigen-binding region of sifaka antibodies exhibited similar amino acid variation with respect to haplorrhine primates. Our study represents the first attempt to characterize sequence diversity of the expressed antibody repertoire in a species of lemur. We anticipate that methods similar to ours will provide the framework for investigating the adaptive immune response in wild populations of other nonmodel organisms and can be used to advance the burgeoning field of eco-immunology. © 2014 John Wiley & Sons Ltd.

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September 22, 2019  |  

Computational analysis of alternative splicing in plant genomes.

Computational analyses play crucial roles in characterizing splicing isoforms in plant genomes. In this review, we provide a survey of computational tools used in recently published, genome-scale splicing analyses in plants. We summarize the commonly used software and pipelines for read mapping, isoform reconstruction, isoform quantification, and differential expression analysis. We also discuss methods for analyzing long reads and the strategies to combine long and short reads in identifying splicing isoforms. We review several tools for characterizing local splicing events, splicing graphs, coding potential, and visualizing splicing isoforms. We further discuss the procedures for identifying conserved splicing isoforms across plant species. Finally, we discuss the outlook of integrating other genomic data with splicing analyses to identify regulatory mechanisms of AS on genome-wide scale. Copyright © 2018 Elsevier B.V. All rights reserved.

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