scholarly journals Refgenie: a reference genome resource manager

2019 ◽  
Author(s):  
Michal Stolarczyk ◽  
Vincent P. Reuter ◽  
Neal E. Magee ◽  
Nathan C. Sheffield

Reference genome assemblies are essential for high-throughput sequencing analysis projects. Typically, genome assemblies are stored on disk alongside related resources; for example, many sequence aligners require the assembly to be indexed. The resulting indexes are broadly applicable for downstream analysis, so it makes sense to share them. However, there is no simple tool to do this. To this end, we introduce refgenie, a reference genome assembly asset manager. Refgenie makes it easier to organize, retrieve, and share genome analysis resources. In addition to genome indexes, refgenie can manage any files related to reference genomes, including sequences and annotation files. Refgenie includes a command-line interface and a server application that provides a RESTful API, so it is useful for both tool development and analysis.Availabilityhttps://refgenie.databio.org

GigaScience ◽  
2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Michał Stolarczyk ◽  
Vincent P Reuter ◽  
Jason P Smith ◽  
Neal E Magee ◽  
Nathan C Sheffield

Abstract Background Reference genome assemblies are essential for high-throughput sequencing analysis projects. Typically, genome assemblies are stored on disk alongside related resources; e.g., many sequence aligners require the assembly to be indexed. The resulting indexes are broadly applicable for downstream analysis, so it makes sense to share them. However, there is no simple tool to do this. Results Here, we introduce refgenie, a reference genome assembly asset manager. Refgenie makes it easier to organize, retrieve, and share genome analysis resources. In addition to genome indexes, refgenie can manage any files related to reference genomes, including sequences and annotation files. Refgenie includes a command line interface and a server application that provides a RESTful API, so it is useful for both tool development and analysis. Conclusions Refgenie streamlines sharing genome analysis resources among groups and across computing environments. Refgenie is available at https://refgenie.databio.org.


2019 ◽  
Vol 35 (21) ◽  
pp. 4389-4391
Author(s):  
Cory Y McLean ◽  
Yeongwoo Hwang ◽  
Ryan Poplin ◽  
Mark A DePristo

Abstract Summary Reference genomes are refined to reflect error corrections and other improvements. While this process improves novel data generation and analysis, incorporating data analyzed on an older reference genome assembly requires transforming the coordinates and representations of the data to the new assembly. Multiple tools exist to perform this transformation for coordinate-only data types, but none supports accurate transformation of genome-wide short variation. Here we present GenomeWarp, a tool for efficiently transforming variants between genome assemblies. GenomeWarp transforms regions and short variants in a conservative manner to minimize false positive and negative variants in the target genome, and converts over 99% of regions and short variants from a representative human genome. Availability and implementation GenomeWarp is written in Java. All source code and the user manual are freely available at https://github.com/verilylifesciences/genomewarp. Supplementary information Supplementary data are available at Bioinformatics online.


BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Gokhan Yavas ◽  
Huixiao Hong ◽  
Wenming Xiao

Abstract Background Accurate de novo genome assembly has become reality with the advancements in sequencing technology. With the ever-increasing number of de novo genome assembly tools, assessing the quality of assemblies has become of great importance in genome research. Although many quality metrics have been proposed and software tools for calculating those metrics have been developed, the existing tools do not produce a unified measure to reflect the overall quality of an assembly. Results To address this issue, we developed the de novo Assembly Quality Evaluation Tool (dnAQET) that generates a unified metric for benchmarking the quality assessment of assemblies. Our framework first calculates individual quality scores for the scaffolds/contigs of an assembly by aligning them to a reference genome. Next, it computes a quality score for the assembly using its overall reference genome coverage, the quality score distribution of its scaffolds and the redundancy identified in it. Using synthetic assemblies randomly generated from the latest human genome build, various builds of the reference genomes for five organisms and six de novo assemblies for sample NA24385, we tested dnAQET to assess its capability for benchmarking quality evaluation of genome assemblies. For synthetic data, our quality score increased with decreasing number of misassemblies and redundancy and increasing average contig length and coverage, as expected. For genome builds, dnAQET quality score calculated for a more recent reference genome was better than the score for an older version. To compare with some of the most frequently used measures, 13 other quality measures were calculated. The quality score from dnAQET was found to be better than all other measures in terms of consistency with the known quality of the reference genomes, indicating that dnAQET is reliable for benchmarking quality assessment of de novo genome assemblies. Conclusions The dnAQET is a scalable framework designed to evaluate a de novo genome assembly based on the aggregated quality of its scaffolds (or contigs). Our results demonstrated that dnAQET quality score is reliable for benchmarking quality assessment of genome assemblies. The dnQAET can help researchers to identify the most suitable assembly tools and to select high quality assemblies generated.


2019 ◽  
Vol 20 (10) ◽  
pp. 2483 ◽  
Author(s):  
Veronika Kapustová ◽  
Zuzana Tulpová ◽  
Helena Toegelová ◽  
Petr Novák ◽  
Jiří Macas ◽  
...  

Reference genomes of important cereals, including barley, emmer wheat and bread wheat, were released recently. Their comparison with genome size estimates obtained by flow cytometry indicated that the assemblies represent not more than 88–98% of the complete genome. This work is aimed at identifying the missing parts in two cereal genomes and proposing techniques to make the assemblies more complete. We focused on tandemly organised repetitive sequences, known to be underrepresented in genome assemblies generated from short-read sequence data. Our study found arrays of three tandem repeats with unit sizes of 1242 to 2726 bp present in the bread wheat reference genome generated from short reads. However, this and another wheat genome assembly employing long PacBio reads failed in integrating correctly the 2726-bp repeat in the pseudomolecule context. This suggests that tandem repeats of this size, frequently incorporated in unassigned scaffolds, may contribute to shrinking of pseudomolecules without reducing size of the entire assembly. We demonstrate how this missing information may be added to the pseudomolecules with the aid of nanopore sequencing of individual BAC clones and optical mapping. Using the latter technique, we identified and localised a 470-kb long array of 45S ribosomal DNA absent from the reference genome of barley.


2021 ◽  
Author(s):  
Jeremie S. Kim ◽  
Can Firtina ◽  
Meryem Banu Cavlak ◽  
Damla Senol Cali ◽  
Nastaran Hajinazar ◽  
...  

AbstractAs genome sequencing tools and techniques improve, researchers are able to incrementally assemble more accurate reference genomes, which enable sensitivity in read mapping and downstream analysis such as variant calling. A more sensitive downstream analysis is critical for a better understanding of the genome donor (e.g., health characteristics). Therefore, read sets from sequenced samples should ideally be mapped to the latest available reference genome that represents the most relevant population. Unfortunately, the increasingly large amount of available genomic data makes it prohibitively expensive to fully re-map each read set to its respective reference genome every time the reference is updated. There are several tools that attempt to accelerate the process of updating a read data set from one reference to another (i.e., remapping) by 1) identifying regions that appear similarly between two references and 2) updating the mapping location of reads that map to any of the identified regions in the old reference to the corresponding similar region in the new reference. The main drawback of existing approaches is that if a read maps to a region in the old reference that does not appear with a reasonable degree of similarity in the new reference, the read cannot be remapped. We find that, as a result of this drawback, a significant portion of annotations (i.e., coding regions in a genome) are lost when using state-of-the-art remapping tools. To address this major limitation in existing tools, we propose AirLift, a fast and comprehensive technique for remapping alignments from one genome to another. Compared to the state-of-the-art method for remapping reads (i.e., full mapping), AirLift reduces 1) the number of reads (out of the entire read set) that need to be fully mapped to the new reference by up to 99.99% and 2) the overall execution time to remap read sets between two reference genome versions by 6.7×, 6.6×, and 2.8× for large (human), medium (C. elegans), and small (yeast) reference genomes, respectively. We validate our remapping results with GATK and find that AirLift provides similar accuracy in identifying ground truth SNP and INDEL variants as the baseline of fully mapping a read set.Code AvailabilityAirLift source code and readme describing how to reproduce our results are available at https://github.com/CMU-SAFARI/AirLift.


2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Mingming Liu ◽  
Zach N. Adelman ◽  
Kevin M. Myles ◽  
Liqing Zhang

With the rapid development of high throughput sequencing technologies, new transcriptomes can be sequenced for little cost with high coverage. Sequence assembly approaches have been modified to meet the requirements for de novo transcriptomes, which have complications not found in traditional genome assemblies such as variation in coverage for each candidate mRNA and alternative splicing. As a consequence, de novo assembly strategies tend to generate a large number of redundant contigs due to sequence variations, which adversely affects downstream analysis and experiments. In this work we proposed TransPS, a transcriptome post-scaffolding method, to generate high quality, nonredundant de novo transcriptomes. TransPS shows promising results on the test transcriptome datasets, where redundancy is greatly reduced by more than 50% and, at the same time, coverage is improved considerably. The web server and source code are available.


2019 ◽  
Author(s):  
Jina Kim ◽  
Joohon Sung ◽  
Kyudong Han ◽  
Wooseok Lee ◽  
Seyoung Mun ◽  
...  

AbstractStudies have shown that the current human reference genome (GRCh38) might miss information for some populations, but “exactly what we miss” is still elusive due to the lower contiguity of non-reference genomes. We juxtaposed the GRCh38 with high contiguity genome assemblies, AK1, to show that ∼1.8% (∼53.4 Mbp) of AK1 sequences missed in GRCh38 with ∼0.76% (∼22.2 Mbp) of ectopic chromosomes. The unique AK1 sequences harbored ∼1,390 putative coding elements. We found that ∼5.3Mb (∼0.2%) of the AK1 sequences aligned and recovered the “unmapped” reads of fourteen individuals (5 East-Asians, 4 Europeans, and 5 Africans) as a reference. The regions that “unmapped” reads aligned included 110 common (shared between ≥2 individuals) and 38 globally (≥7 individuals) missing regions with 25 candidate coding elements. We verified that many of the common missing regions exist in multiple populations and chimpanzee’s DNA. Our study illuminates not only the discovery of missing information but the use of highly precise ethnic genomes in understanding human genetics.


2020 ◽  
Author(s):  
Carlos Valiente-Mullor ◽  
Beatriz Beamud ◽  
Iván Ansari ◽  
Carlos Francés-Cuesta ◽  
Neris García-González ◽  
...  

AbstractMapping of high-throughput sequencing (HTS) reads to a single arbitrary reference genome is a frequently used approach in microbial genomics. However, the choice of a reference may represent a source of errors that may affect subsequent analyses such as the detection of single nucleotide polymorphisms (SNPs) and phylogenetic inference. In this work, we evaluated the effect of reference choice on short-read sequence data from five clinically and epidemiologically relevant bacteria (Klebsiella pneumoniae, Legionella pneumophila, Neisseria gonorrhoeae, Pseudomonas aeruginosa and Serratia marcescens). Publicly available whole-genome assemblies encompassing the genomic diversity of these species were selected as reference sequences, and read alignment statistics, SNP calling, recombination rates, dN/dS ratios, and phylogenetic trees were evaluated depending on the mapping reference. The choice of different reference genomes proved to have an impact on almost all the parameters considered in the five species. In addition, these biases had potential epidemiological implications such as including/excluding isolates of particular clades and the estimation of genetic distances. These findings suggest that the single reference approach might introduce systematic errors during mapping that affect subsequent analyses, particularly for data sets with isolates from genetically diverse backgrounds. In any case, exploring the effects of different references on the final conclusions is highly recommended.Author summaryMapping consists in the alignment of reads (i.e., DNA fragments) obtained through high-throughput genome sequencing to a previously assembled reference sequence. It is a common practice in genomic studies to use a single reference for mapping, usually the ‘reference genome’ of a species —a high-quality assembly. However, the selection of an optimal reference is hindered by intrinsic intra-species genetic variability, particularly in bacteria. Biases/errors due to reference choice for mapping in bacteria have been identified. These are mainly originated in alignment errors due to genetic differences between the reference genome and the read sequences. Eventually, they could lead to misidentification of variants and biased reconstruction of phylogenetic trees (which reflect ancestry between different bacterial lineages). However, a systematic work on the effects of reference choice in different bacterial species is still missing, particularly regarding its impact on phylogenies. This work intended to fill that gap. The impact of reference choice has proved to be pervasive in the five bacterial species that we have studied and, in some cases, alterations in phylogenetic trees could lead to incorrect epidemiological inferences. Hence, the use of different reference genomes may be prescriptive to assess the potential biases of mapping.


2020 ◽  
Author(s):  
Carol Moraga ◽  
Evelyn Sanchez ◽  
Mariana Galvão Ferrarini ◽  
Rodrigo A. Gutierrez ◽  
Elena A. Vidal ◽  
...  

AbstractMicroRNAs (miRNAs) are small non-coding RNAs that are key players in the regulation of gene expression. In the last decade, with the increasing accessibility of high-throughput sequencing technologies, different methods have been developed to identify miRNAs, most of which rely on pre-existing reference genomes. However, when a reference genome is absent or is not of high quality, such identification becomes more difficult. In this context, we developed BrumiR, an algorithm that is able to discover miRNAs directly and exclusively from sRNA-seq data. We benchmarked BrumiR with datasets encompassing animal and plant species using real and simulated sRNA-seq experiments. The results demonstrate that BrumiR reaches the highest recall for miRNA discovery, while at the same time being much faster and more efficient than the state-of-the-art tools evaluated. The latter allows BrumiR to analyze a large number of sRNA-seq experiments, from plants or animals species. Moreover, BrumiR detects additional information regarding other expressed sequences (sRNAs, isomiRs, etc.), thus maximizing the biological insight gained from sRNA-seq experiments. Finally, when a reference genome is available, BrumiR provides a new mapping tool (BrumiR2ref) that performs an a posteriori exhaustive search to identify the precursor sequences. The code of BrumiR is freely available at https://github.com/camoragaq/BrumiR.


F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1338 ◽  
Author(s):  
Steven W. Wingett ◽  
Simon Andrews

DNA sequencing analysis typically involves mapping reads to just one reference genome.  Mapping against multiple genomes is necessary, however, when the genome of origin requires confirmation. Mapping against multiple genomes is also advisable for detecting contamination or for identifying sample swaps which, if left undetected, may lead to incorrect experimental conclusions.  Consequently, we present FastQ Screen, a tool to validate the origin of DNA samples by quantifying the proportion of reads that map to a panel of reference genomes. FastQ Screen is intended to be used routinely as a quality control measure and for analysing samples in which the origin of the DNA is uncertain or has multiple sources.


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