Accurate sequencing data is key for University of Florida scientist Ana Conesa. She is using PacBio HiFi reads from the Sequel II System to identity alternative isoforms and determine the…
En este seminario online Carmen Guarco, Senior Field Application Scientist especializada en Bioinformática en PacBio, ofrece una introducción a la tecnología de secuenciación SMRT, las secuencias HiFi y sus aplicaciones…
In this ASHG 2020 CoLab presentation hear Principal Scientists, Aaron Wenger and Elizabeth Tseng share how highly accurate long reads (HiFi reads) provide comprehensive variant detection for both genomes and…
Long-read mRNA sequencing such as PacBio’s Iso-Seq method offer high-throughput transcriptome profiling that circumvents the transcript assembly problem by sequencing full-length cDNA. The Iso-Seq method has emerged as the most…
In this SMRT Leiden 2020 Online Virtual Event presentation Pedro Oliveira of Mount Sinai shares his research on Clostridioides – a leading cause of nosocomial-acquired diarrhea and colitis across the…
In this SMRT Leiden 2020 Online Virtual Event presentation, Ana Conesa of the University of Florida shares her work on the latest updates to the Funciitonal Iso-Annot software, that includes…
In this SMRT Leiden 2020 Online Virtual Event presentation, Richard Kuo of The Roslin Institute shares his work on using Iso-Seq data to gain a better understanding of the biology…
Most genes in eukaryotic organisms produce alternative isoforms, broadening the diversity of proteins and non-coding RNAs encoded by the genome. In contrast to other RNA sequencing platforms that rely on…
In this ASHG 2020 PacBio Workshop Hagen Tilgner of Cornell University shares how he has used single-cell RNA sequencing using long reads to identify novel isoform expression in brain tissues.
In this ASHG 2020 PacBio Workshop Jonas Korlach, CSO, shares how the new PacBio Sequel IIe System makes highly accurate long-read sequencing easy and affordable so?all scientists can gain comprehensive…
N6-methyladenosine (m6A) is a widespread RNA modification that influences nearly every aspect of the messenger RNA lifecycle. Our understanding of m6A has been facilitated by the development of global m6A mapping methods, which use antibodies to immunoprecipitate methylated RNA. However, these methods have several limitations, including high input RNA requirements and cross-reactivity to other RNA modifications. Here, we present DART-seq (deamination adjacent to RNA modification targets), an antibody-free method for detecting m6A sites. In DART-seq, the cytidine deaminase APOBEC1 is fused to the m6A-binding YTH domain. APOBEC1-YTH expression in cells induces C-to-U deamination at sites adjacent to m6A residues, which are detected using standard RNA-seq. DART-seq identifies thousands of m6A sites in cells from as little as 10?ng of total RNA and can detect m6A accumulation in cells over time. Additionally, we use long-read DART-seq to gain insights into m6A distribution along the length of individual transcripts.
The widespread occurrence of repetitive stretches of DNA in genomes of organisms across the tree of life imposes fundamental challenges for sequencing, genome assembly, and automated annotation of genes and proteins. This multi-level problem can lead to errors in genome and protein databases that are often not recognized or acknowledged. As a consequence, end users working with sequences with repetitive regions are faced with ‘ready-to-use’ deposited data whose trustworthiness is difficult to determine, let alone to quantify. Here, we provide a review of the problems associated with tandem repeat sequences that originate from different stages during the sequencing-assembly-annotation-deposition workflow, and that may proliferate in public database repositories affecting all downstream analyses. As a case study, we provide examples of the Atlantic cod genome, whose sequencing and assembly were hindered by a particularly high prevalence of tandem repeats. We complement this case study with examples from other species, where mis-annotations and sequencing errors have propagated into protein databases. With this review, we aim to raise the awareness level within the community of database users, and alert scientists working in the underlying workflow of database creation that the data they omit or improperly assemble may well contain important biological information valuable to others. © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.
The domestic pig (Sus scrofa) is important both as a food source and as a biomedical model with high anatomical and immunological similarity to humans. The draft reference genome (Sscrofa10.2) represented a purebred female pig from a commercial pork production breed (Duroc), and was established using older clone-based sequencing methods. The Sscrofa10.2 assembly was incomplete and unresolved redundancies, short range order and orientation errors and associated misassembled genes limited its utility. We present two highly contiguous chromosome-level genome assemblies created with more recent long read technologies and a whole genome shotgun strategy, one for the same Duroc female (Sscrofa11.1) and one for an outbred, composite breed male animal commonly used for commercial pork production (USMARCv1.0). Both assemblies are of substantially higher (>90-fold) continuity and accuracy compared to the earlier reference, and the availability of two independent assemblies provided an opportunity to identify large-scale variants and to error-check the accuracy of representation of the genome. We propose that the improved Duroc breed assembly (Sscrofa11.1) become the reference genome for genomic research in pigs.
Bread wheat (Triticum aestivum L.) is an allohexaploid, and the transcriptional characteristics of the wheat embryo and endosperm during grain development remain unclear. To analyze the transcriptome, we performed isoform sequencing (Iso-Seq) for wheat grain and RNA sequencing (RNA-Seq) for the embryo and de-embryonated kernels. The differential regulation between the embryo and de-embryonated kernels was found to be greater than the difference between the two time points for each tissue. Exactly 2264 and 4790 tissue-specific genes were found at 14 days post-anthesis (DPA), while 5166 and 3784 genes were found at 25 DPA in the embryo and de-embryonated kernels, respectively. Genes expressed in the embryo were more likely to be related to nucleic acid and enzyme regulation. In de-embryonated kernels, genes were rich in substance metabolism and enzyme activity functions. Moreover, 4351, 4641, 4516, and 4453 genes with the A, B, and D homoeoloci were detected for each of the four tissues. Expression characteristics suggested that the D genome may be the largest contributor to the transcriptome in developing grain. Among these, 48, 66, and 38 silenced genes emerged in the A, B, and D genomes, respectively. Gene ontology analysis showed that silenced genes could be inclined to different functions in different genomes. Our study provided specific gene pools of the embryo and de-embryonated kernels and a homoeolog expression bias model on a large scale. This is helpful for providing new insights into the molecular physiology of wheat.
Over the past decade, RNA sequencing (RNA-seq) has become an indispensable tool for transcriptome-wide analysis of differential gene expression and differential splicing of mRNAs. However, as next-generation sequencing technologies have developed, so too has RNA-seq. Now, RNA-seq methods are available for studying many different aspects of RNA biology, including single-cell gene expression, translation (the translatome) and RNA structure (the structurome). Exciting new applications are being explored, such as spatial transcriptomics (spatialomics). Together with new long-read and direct RNA-seq technologies and better computational tools for data analysis, innovations in RNA-seq are contributing to a fuller understanding of RNA biology, from questions such as when and where transcription occurs to the folding and intermolecular interactions that govern RNA function.
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