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Asset Tag: bioinformatics

April 21, 2020

Analysis of differential gene expression and alternative splicing is significantly influenced by choice of reference genome.

RNA-seq analysis has enabled the evaluation of transcriptional changes in many species including nonmodel organisms. However, in most species only a single reference genome is available and RNA-seq reads from highly divergent varieties are typically aligned to this reference. Here, we quantify the impacts of the choice of mapping genome in rice where three high-quality reference genomes are available. We aligned RNA-seq data from a popular productive rice variety to three different reference genomes and found that the identification of differentially expressed genes differed depending on which reference genome was used for mapping. Furthermore, the ability to detect differentially used transcript isoforms was profoundly affected by the choice of reference genome: Only 30% of the differentially used splicing features were detected when reads were mapped to the more commonly used, but more distantly related reference genome. This demonstrated that gene expression and splicing analysis varies considerably depending on the mapping reference genome, and that analysis of individuals that are distantly related to an available reference genome may be improved by acquisition of new genomic reference material. We observed that these differences in transcriptome analysis are, in part, due to the presence of single nucleotide polymorphisms between the sequenced individual and each respective reference genome, as well as annotation differences between the reference genomes that exist even between syntenic orthologs. We conclude that even between two closely related genomes of similar quality, using the reference genome that is most closely related to the species being sampled significantly improves transcriptome analysis. © 2019 Slabaugh et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

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April 21, 2020

Characterizing the major structural variant alleles of the human genome.

In order to provide a comprehensive resource for human structural variants (SVs), we generated long-read sequence data and analyzed SVs for fifteen human genomes. We sequence resolved 99,604 insertions, deletions, and inversions including 2,238 (1.6 Mbp) that are shared among all discovery genomes with an additional 13,053 (6.9 Mbp) present in the majority, indicating minor alleles or errors in the reference. Genotyping in 440 additional genomes confirms the most common SVs in unique euchromatin are now sequence resolved. We report a ninefold SV bias toward the last 5 Mbp of human chromosomes with nearly 55% of all VNTRs (variable number of tandem repeats) mapping to this portion of the genome. We identify SVs affecting coding and noncoding regulatory loci improving annotation and interpretation of functional variation. These data provide the framework to construct a canonical human reference and a resource for developing advanced representations capable of capturing allelic diversity. Copyright © 2018 Elsevier Inc. All rights reserved.

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April 21, 2020

An open resource for accurately benchmarking small variant and reference calls.

Benchmark small variant calls are required for developing, optimizing and assessing the performance of sequencing and bioinformatics methods. Here, as part of the Genome in a Bottle (GIAB) Consortium, we apply a reproducible, cloud-based pipeline to integrate multiple short- and linked-read sequencing datasets and provide benchmark calls for human genomes. We generate benchmark calls for one previously analyzed GIAB sample, as well as six genomes from the Personal Genome Project. These new genomes have broad, open consent, making this a ‘first of its kind’ resource that is available to the community for multiple downstream applications. We produce 17% more benchmark single nucleotide variations, 176% more indels and 12% larger benchmark regions than previously published GIAB benchmarks. We demonstrate that this benchmark reliably identifies errors in existing callsets and highlight challenges in interpreting performance metrics when using benchmarks that are not perfect or comprehensive. Finally, we identify strengths and weaknesses of callsets by stratifying performance according to variant type and genome context.

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April 21, 2020

Assembly of long, error-prone reads using repeat graphs.

Accurate genome assembly is hampered by repetitive regions. Although long single molecule sequencing reads are better able to resolve genomic repeats than short-read data, most long-read assembly algorithms do not provide the repeat characterization necessary for producing optimal assemblies. Here, we present Flye, a long-read assembly algorithm that generates arbitrary paths in an unknown repeat graph, called disjointigs, and constructs an accurate repeat graph from these error-riddled disjointigs. We benchmark Flye against five state-of-the-art assemblers and show that it generates better or comparable assemblies, while being an order of magnitude faster. Flye nearly doubled the contiguity of the human genome assembly (as measured by the NGA50 assembly quality metric) compared with existing assemblers.

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April 21, 2020

Efficiency of PacBio long read correction by 2nd generation Illumina sequencing.

Long sequencing reads offer unprecedented opportunities in analysis and reconstruction of complex genomic regions. However, the gain in sequence length is often traded for quality. Therefore, recently several approaches have been proposed (e.g. higher sequencing coverage, hybrid assembly or sequence correction) to enhance the quality of long sequencing reads. A simple and cost-effective approach includes use of the high quality 2nd generation sequencing data to improve the quality of long reads. We designed a dedicated testing procedure and selected universal programs for long read correction, which provide as the output sequences that can be used in further genomic and transcriptomic studies. Our results show that HALC is the best choice for correction of long PacBio reads, when both, read size and quality, are the main focus of the analysis. However, the tested tools show some unexpected behaviors, including read trimming and fragmentation.Copyright © 2017 Elsevier Inc. All rights reserved.

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April 21, 2020

Whole-Genome Alignment and Comparative Annotation.

Rapidly improving sequencing technology coupled with computational developments in sequence assembly are making reference-quality genome assembly economical. Hundreds of vertebrate genome assemblies are now publicly available, and projects are being proposed to sequence thousands of additional species in the next few years. Such dense sampling of the tree of life should give an unprecedented new understanding of evolution and allow a detailed determination of the events that led to the wealth of biodiversity around us. To gain this knowledge, these new genomes must be compared through genome alignment (at the sequence level) and comparative annotation (at the gene level). However, different alignment and annotation methods have different characteristics; before starting a comparative genomics analysis, it is important to understand the nature of, and biases and limitations inherent in, the chosen methods. This review is intended to act as a technical but high-level overview of the field that should provide this understanding. We briefly survey the state of the genome alignment and comparative annotation fields and potential future directions for these fields in a new, large-scale era of comparative genomics.

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April 21, 2020

Discovery of tandem and interspersed segmental duplications using high-throughput sequencing.

Several algorithms have been developed that use high-throughput sequencing technology to characterize structural variations (SVs). Most of the existing approaches focus on detecting relatively simple types of SVs such as insertions, deletions and short inversions. In fact, complex SVs are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of complex SVs to human disease, we need new algorithms to accurately discover and genotype such variants. Additionally, due to similar sequencing signatures, inverted duplications or gene conversion events that include inverted segmental duplications are often characterized as simple inversions, likewise, duplications and gene conversions in direct orientation may be called as simple deletions. Therefore, there is still a need for accurate algorithms to fully characterize complex SVs and thus improve calling accuracy of more simple variants.We developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing datasets. We integrated these methods to our TARDIS tool, which is now capable of detecting various types of SVs using multiple sequence signatures such as read pair, read depth and split read. We evaluated the prediction performance of our algorithms through several experiments using both simulated and real datasets. In the simulation experiments, using a 30× coverage TARDIS achieved 96% sensitivity with only 4% false discovery rate. For experiments that involve real data, we used two haploid genomes (CHM1 and CHM13) and one human genome (NA12878) from the Illumina Platinum Genomes set. Comparison of our results with orthogonal PacBio call sets from the same genomes revealed higher accuracy for TARDIS than state-of-the-art methods. Furthermore, we showed a surprisingly low false discovery rate of our approach for discovery of tandem, direct and inverted interspersed segmental duplications prediction on CHM1 (<5% for the top 50 predictions).TARDIS source code is available at https://github.com/BilkentCompGen/tardis, and a corresponding Docker image is available at https://hub.docker.com/r/alkanlab/tardis/.Supplementary data are available at Bioinformatics online. © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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April 21, 2020

Long-Read Annotation: Automated Eukaryotic Genome Annotation Based on Long-Read cDNA Sequencing.

Single-molecule full-length complementary DNA (cDNA) sequencing can aid genome annotation by revealing transcript structure and alternative splice forms, yet current annotation pipelines do not incorporate such information. Here we present long-read annotation (LoReAn) software, an automated annotation pipeline utilizing short- and long-read cDNA sequencing, protein evidence, and ab initio prediction to generate accurate genome annotations. Based on annotations of two fungal genomes (Verticillium dahliae and Plicaturopsis crispa) and two plant genomes (Arabidopsis [Arabidopsis thaliana] and Oryza sativa), we show that LoReAn outperforms popular annotation pipelines by integrating single-molecule cDNA-sequencing data generated from either the Pacific Biosciences or MinION sequencing platforms, correctly predicting gene structure, and capturing genes missed by other annotation pipelines. © 2019 American Society of Plant Biologists. All Rights Reserved.

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April 21, 2020

Fast and accurate genomic analyses using genome graphs.

The human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, thus impairing analysis accuracy. Here we present a graph reference genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million insertions and deletions (indels). The pipeline processes one whole-genome sequencing sample in 6.5?h using a system with 36?CPU cores. We show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, with unaffected specificity. Structural variations incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is an important advance toward fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.

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April 21, 2020

rMETL: sensitive mobile element insertion detection with long read realignment.

Mobile element insertion (MEI) is a major category of structure variations (SVs). The rapid development of long read sequencing technologies provides the opportunity to detect MEIs sensitively. However, the signals of MEI implied by noisy long reads are highly complex due to the repetitiveness of mobile elements as well as the high sequencing error rates. Herein, we propose the Realignment-based Mobile Element insertion detection Tool for Long read (rMETL). Benchmarking results of simulated and real datasets demonstrate that rMETL enables to handle the complex signals to discover MEIs sensitively. It is suited to produce high-quality MEI callsets in many genomics studies.rMETL is available from https://github.com/hitbc/rMETL.Supplementary data are available at Bioinformatics online. © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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April 21, 2020

TranscriptClean: variant-aware correction of indels, mismatches and splice junctions in long-read transcripts.

Long-read, single-molecule sequencing platforms hold great potential for isoform discovery and characterization of multi-exon transcripts. However, their high error rates are an obstacle to distinguishing novel transcript isoforms from sequencing artifacts. Therefore, we developed the package TranscriptClean to correct mismatches, microindels and noncanonical splice junctions in mapped transcripts using the reference genome while preserving known variants.Our method corrects nearly all mismatches and indels present in a publically available human PacBio Iso-seq dataset, and rescues 39% of noncanonical splice junctions.All Python and R scripts used in this paper are available at https://github.com/dewyman/TranscriptClean.

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April 21, 2020

TaF: a web platform for taxonomic profile-based fungal gene prediction.

The accurate prediction and annotation of gene structures from the genome sequence of an organism enable genome-wide functional analyses to obtain insight into the biological properties of an organism.We recently developed a highly accurate filamentous fungal gene prediction pipeline and web platform called TaF. TaF is a homology-based gene predictor employing large-scale taxonomic profiling to search for close relatives in genome queries.TaF pipeline consists of four processing steps; (1) taxonomic profiling to search for close relatives to query, (2) generation of hints for determining exon-intron boundaries from orthologous protein sequence data of the profiled species, (3) gene prediction by combination of ab inito and evidence-based prediction methods, and (4) homology search for gene models.TaF generates extrinsic evidence that suggests possible exon-intron boundaries based on orthologous protein sequence data, thus reducing false-positive predictions of gene structure based on distantly related orthologs data. In particular, the gene prediction method using taxonomic profiling shows very high accuracy, including high sensitivity and specificity for gene models, suggesting a new approach for homology-based gene prediction from newly sequenced or uncharacterized fungal genomes, with the potential to improve the quality of gene prediction.TaF will be a useful tool for fungal genome-wide analyses, including the identification of targeted genes associated with a trait, transcriptome profiling, comparative genomics, and evolutionary analysis.

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April 21, 2020

The interplay between microRNA and alternative splicing of linear and circular RNAs in eleven plant species.

MicroRNA (miRNA) and alternative splicing (AS)-mediated post-transcriptional regulation has been extensively studied in most eukaryotes. However, the interplay between AS and miRNAs has not been explored in plants. To our knowledge, the overall profile of miRNA target sites in circular RNAs (circRNA) generated by alternative back splicing has never been reported previously. To address the challenge, we identified miRNA target sites located in alternatively spliced regions of the linear and circular splice isoforms using the up-to-date single-molecule real-time (SMRT) isoform sequencing (Iso-Seq) and Illumina sequencing data in eleven plant species.In total, we identified 399?401 and 114?574 AS events from linear and circular RNAs, respectively. Among them, there were 64?781 and 41?146 miRNA target sites located in linear and circular AS region, respectively. In addition, we found 38?913 circRNAs to be overlapping with 45?648 AS events of its own parent isoforms, suggesting circRNA regulation of AS of linear RNAs by forming R-loop with the genomic locus. Here, we present a comprehensive database of miRNA targets in alternatively spliced linear and circRNAs (ASmiR) and a web server for deposition and identification of miRNA target sites located in the alternatively spliced region of linear and circular RNAs. This database is accompanied by an easy-to-use web query interface for meaningful downstream analysis. Plant research community can submit user-defined datasets to the web service to search AS regions harboring small RNA target sites. In conclusion, this study provides an unprecedented resource to understand regulatory relationships between miRNAs and AS in both gymnosperms and angiosperms.The readily accessible database and web-based tools are available at http://forestry.fafu.edu.cn/bioinfor/db/ASmiR.Supplementary data are available at Bioinformatics online. © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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April 21, 2020

MSC: a metagenomic sequence classification algorithm.

Metagenomics is the study of genetic materials directly sampled from natural habitats. It has the potential to reveal previously hidden diversity of microscopic life largely due to the existence of highly parallel and low-cost next-generation sequencing technology. Conventional approaches align metagenomic reads onto known reference genomes to identify microbes in the sample. Since such a collection of reference genomes is very large, the approach often needs high-end computing machines with large memory which is not often available to researchers. Alternative approaches follow an alignment-free methodology where the presence of a microbe is predicted using the information about the unique k-mers present in the microbial genomes. However, such approaches suffer from high false positives due to trading off the value of k with the computational resources. In this article, we propose a highly efficient metagenomic sequence classification (MSC) algorithm that is a hybrid of both approaches. Instead of aligning reads to the full genomes, MSC aligns reads onto a set of carefully chosen, shorter and highly discriminating model sequences built from the unique k-mers of each of the reference sequences.Microbiome researchers are generally interested in two objectives of a taxonomic classifier: (i) to detect prevalence, i.e. the taxa present in a sample, and (ii) to estimate their relative abundances. MSC is primarily designed to detect prevalence and experimental results show that MSC is indeed a more effective and efficient algorithm compared to the other state-of-the-art algorithms in terms of accuracy, memory and runtime. Moreover, MSC outputs an approximate estimate of the abundances.The implementations are freely available for non-commercial purposes. They can be downloaded from https://drive.google.com/open?id=1XirkAamkQ3ltWvI1W1igYQFusp9DHtVl. © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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April 21, 2020

Long-read sequence capture of the haemoglobin gene clusters across codfish species.

Combining high-throughput sequencing with targeted sequence capture has become an attractive tool to study specific genomic regions of interest. Most studies have so far focused on the exome using short-read technology. These approaches are not designed to capture intergenic regions needed to reconstruct genomic organization, including regulatory regions and gene synteny. Here, we demonstrate the power of combining targeted sequence capture with long-read sequencing technology for comparative genomic analyses of the haemoglobin (Hb) gene clusters across eight species separated by up to 70 million years. Guided by the reference genome assembly of the Atlantic cod (Gadus morhua) together with genome information from draft assemblies of selected codfishes, we designed probes covering the two Hb gene clusters. Use of custom-made barcodes combined with PacBio RSII sequencing led to highly continuous assemblies of the LA (~100 kb) and MN (~200 kb) clusters, which include syntenic regions of coding and intergenic sequences. Our results revealed an overall conserved genomic organization of the Hb genes within this lineage, yet with several, lineage-specific gene duplications. Moreover, for some of the species examined, we identified amino acid substitutions at two sites in the Hbb1 gene as well as length polymorphisms in its regulatory region, which has previously been linked to temperature adaptation in Atlantic cod populations. This study highlights the use of targeted long-read capture as a versatile approach for comparative genomic studies by generation of a cross-species genomic resource elucidating the evolutionary history of the Hb gene family across the highly divergent group of codfishes. © 2018 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

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