A comprehensive review of scaffolding methods in genome assembly

2021 ◽  
Author(s):  
Junwei Luo ◽  
Yawei Wei ◽  
Mengna Lyu ◽  
Zhengjiang Wu ◽  
Xiaoyan Liu ◽  
...  
Keyword(s):  
Genome Assembly ◽  
High Throughput Sequencing ◽  
Rapid Development ◽  
Genomic Research ◽  
Future Research ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Biological Studies ◽  
Downstream Analysis

Abstract In the field of genome assembly, scaffolding methods make it possible to obtain a more complete and contiguous reference genome, which is the cornerstone of genomic research. Scaffolding methods typically utilize the alignments between contigs and sequencing data (reads) to determine the orientation and order among contigs and to produce longer scaffolds, which are helpful for genomic downstream analysis. With the rapid development of high-throughput sequencing technologies, diverse types of reads have emerged over the past decade, especially in long-range sequencing, which have greatly enhanced the assembly quality of scaffolding methods. As the number of scaffolding methods increases, biology and bioinformatics researchers need to perform in-depth analyses of state-of-the-art scaffolding methods. In this article, we focus on the difficulties in scaffolding, the differences in characteristics among various kinds of reads, the methods by which current scaffolding methods address these difficulties, and future research opportunities. We hope this work will benefit the design of new scaffolding methods and the selection of appropriate scaffolding methods for specific biological studies.

scholarly journals LRSDAY: Long-read Sequencing Data Analysis for Yeasts

2017 ◽  
Author(s):  
Jia-Xing Yue ◽  
Gianni Liti
Keyword(s):  
Genome Assembly ◽  
Model Organism ◽  
Sequencing Data ◽  
Protein Coding ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
Downstream Analysis ◽  
Eukaryotic Organisms ◽  
Genomic Regions

AbstractLong-read sequencing technologies have become increasingly popular in genome projects due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast, Saccharomyces cerevisiae, has many isolates currently being sequenced with long reads. However, analyzing long-read sequencing data to produce high-quality genome assembly and annotation remains challenging. Here we present LRSDAY, the first one-stop solution to streamline this process. LRSDAY can produce chromosome-level end-to-end genome assembly and comprehensive annotations for various genomic features (including centromeres, protein-coding genes, tRNAs, transposable elements and telomere-associated elements) that are ready for downstream analysis. Although tailored for S. cerevisiae, we designed LRSDAY to be highly modular and customizable, making it adaptable for virtually any eukaryotic organisms. Applying LRSDAY to a S. cerevisiae strain takes ∼43 hrs to generate a complete and well-annotated genome from ∼100X Pacific Biosciences (PacBio) reads using four threads.

scholarly journals AlmostSignificant: Simplifying quality control of high-throughput sequencing data

2016 ◽  
Author(s):  
Joseph Ward ◽  
Christian Cole ◽  
Melanie Febrer ◽  
Geoffrey Barton
Keyword(s):  
Quality Control ◽  
Dna Sequencing ◽  
Illumina Sequencing ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
Multiple Sources ◽  
Meta Data ◽  
Sequencing Technologies ◽  
High Throughput Sequencing Data

AbstractMotivationThe current generation of DNA sequencing technologies produce a large amount of data quickly. All of these data need to pass some form of quality control processing and checking before they can be used for any analysis. The large number of samples that are run through Illumina sequencing machines makes the process of quality control an onerous and time-consuming task that requires multiple pieces of information from several sources.ResultsAlmostSignificant is an open-source platform for aggregating multiple sources of quality metrics as well as meta-data associated with DNA sequencing runs from Illumina sequencing machines. AlmostSignificant is a graphical platform to streamline the quality control of DNA sequencing data, to collect and store these data for future reference and to collect extra meta-data associated with the sequencing runs to check for errors and monitor the volume of data produced by the associated machines. AlmostSignificant has been used to track the quality of over 80 sequencing runs covering over 2500 samples produced over the last three years.AvailabilityThe code and documentation for AlmostSignificant is freely available at https://github.com/bartongroup/[email protected], [email protected]

scholarly journals A Transcriptome Post-Scaffolding Method for Assembling High Quality Contigs

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Mingming Liu ◽  
Zach N. Adelman ◽  
Kevin M. Myles ◽  
Liqing Zhang
Keyword(s):  
High Throughput Sequencing ◽  
De Novo ◽  
Rapid Development ◽  
High Quality ◽  
High Coverage ◽  
Sequencing Technologies ◽  
Sequence Variations ◽  
Downstream Analysis ◽  
Genome Assemblies ◽  
Redundant Contigs

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.

scholarly journals Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

MycoKeys ◽  
2018 ◽  
Vol 39 ◽  
pp. 29-40 ◽  
Author(s):  
Sten Anslan ◽  
R. Henrik Nilsson ◽  
Christian Wurzbacher ◽  
Petr Baldrian ◽  
Leho Tedersoo ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Computation Time ◽  
Potential Effect ◽  
Data Sets ◽  
Sequencing Data ◽  
Operational Taxonomic Units ◽  
High Throughput Sequencing Data ◽  
Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.

scholarly journals Identifying the causes and consequences of assembly gaps using a multiplatform genome assembly of a bird-of-paradise

2019 ◽  
Author(s):  
Valentina Peona ◽  
Mozes P.K. Blom ◽  
Luohao Xu ◽  
Reto Burri ◽  
Shawn Sullivan ◽  
...  
Keyword(s):  
Dark Matter ◽  
Genome Assembly ◽  
Sex Chromosome ◽  
De Novo ◽  
Model Organism ◽  
Technology Choice ◽  
High Quality ◽  
Sequencing Technologies ◽  
Downstream Analysis ◽  
Genome Assemblies

AbstractGenome assemblies are currently being produced at an impressive rate by consortia and individual laboratories. The low costs and increasing efficiency of sequencing technologies have opened up a whole new world of genomic biodiversity. Although these technologies generate high-quality genome assemblies, there are still genomic regions difficult to assemble, like repetitive elements and GC-rich regions (genomic “dark matter”). In this study, we compare the efficiency of currently used sequencing technologies (short/linked/long reads and proximity ligation maps) and combinations thereof in assembling genomic dark matter starting from the same sample. By adopting different de-novo assembly strategies, we were able to compare each individual draft assembly to a curated multiplatform one and identify the nature of the previously missing dark matter with a particular focus on transposable elements, multi-copy MHC genes, and GC-rich regions. Thanks to this multiplatform approach, we demonstrate the feasibility of producing a high-quality chromosome-level assembly for a non-model organism (paradise crow) for which only suboptimal samples are available. Our approach was able to reconstruct complex chromosomes like the repeat-rich W sex chromosome and several GC-rich microchromosomes. Telomere-to-telomere assemblies are not a reality yet for most organisms, but by leveraging technology choice it is possible to minimize genome assembly gaps for downstream analysis. We provide a roadmap to tailor sequencing projects around the completeness of both the coding and non-coding parts of the genomes.

scholarly journals TrancriptomeReconstructoR, A Data-Driven Annotation of Complex Transcriptomes

2020 ◽  
Author(s):  
Maxim Ivanov ◽  
Albin Sandelin ◽  
Sebastian Marquardt
Keyword(s):  
De Novo ◽  
Gene Annotation ◽  
R Package ◽  
Sequence Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Model ◽  
Preparation Methods ◽  
Downstream Analysis

Abstract Background: The quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data. Results: We developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: i) full-length RNA-seq for detection of splicing patterns and ii) high-throughput 5' and 3' tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts.We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the two most commonly used community gene models, TAIR10 and Araport11. In particular, we identify thousands of transient transcripts missing from the existing annotations. Our new annotation promises to improve the quality of A.thaliana genome research.Conclusions: Our proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

scholarly journals Utilizing the VirIdAl Pipeline to Search for Viruses in the Metagenomic Data of Bat Samples

Viruses ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2006
Author(s):  
Anna Y Budkina ◽  
Elena V Korneenko ◽  
Ivan A Kotov ◽  
Daniil A Kiselev ◽  
Ilya V Artyushin ◽  
...  
Keyword(s):  
Large Scale ◽  
High Throughput Sequencing ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Viral Pathogens ◽  
Genomic Databases ◽  
Bioinformatic Pipeline ◽  
Viral Genomes ◽  
Sequencing Technologies ◽  
Viral Screening

According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This problem particularly impedes viral screening, due to vast heterogeneity in viral genomes. In this paper, we present a new bioinformatic pipeline, VirIdAl, for detecting and identifying viral pathogens in sequencing data. We also demonstrate the utility of the new software by applying it to viral screening of the feces of bats collected in the Moscow region, which revealed a significant variety of viruses associated with bats, insects, plants, and protozoa. The presence of alpha and beta coronavirus reads, including the MERS-like bat virus, deserves a special mention, as it once again indicates that bats are indeed reservoirs for many viral pathogens. In addition, it was shown that alignment-based methods were unable to identify the taxon for a large proportion of reads, and we additionally applied other approaches, showing that they can further reveal the presence of viral agents in sequencing data. However, the incompleteness of viral databases remains a significant problem in the studies of viral diversity, and therefore necessitates the use of combined approaches, including those based on machine learning methods.

scholarly journals HTSeq - A Python framework to work with high-throughput sequencing data

2014 ◽  
Author(s):  
Simon Anders ◽  
Paul Theodor Pyl ◽  
Wolfgang Huber
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Rapid Development ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Standard Work ◽  
Data Formats ◽  
High Throughput Sequencing Data ◽  
Python Package

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard work flows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data such as genomic coordinates, sequences, sequencing reads, alignments, gene model information, variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability: HTSeq is released as open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index, https://pypi.python.org/pypi/HTSeq

Trends and Research of Wikis' Quality and Governance Based on Bibliometric and Content Analysis

2014 ◽  
pp. 148-166
Author(s):  
Qinghua Zhu ◽  
Linghe Huang ◽  
Jia Tina Du ◽  
Hua Liu
Keyword(s):  
Content Analysis ◽  
Information Science ◽  
Rapid Development ◽  
Active Role ◽  
Future Research ◽  
Organization Management ◽  
Future Research Agenda ◽  
The Status ◽  
And Control

Wiki is a typical representative of the User-Generated Content. Its appearance greatly promotes the creation, organization, management, and sharing of knowledge on the Internet. As articles grow rapidly in Wikis, the quality of the articles has aroused many people’s concerns. The topics on how to assess and control the quality of articles have attracted many researchers. However, few studies have been conducted to investigate the status of this research topic. This chapter explores the current research status and trends of wikis' quality and governance. The authors selected papers from the databases of ISI, EI, IEEE, and other widely used databases. They reported the trends and research of wikis’ quality and governance using bibliometric analysis and content analysis of a total of 99 relevant papers. The results show that although the research topics in the field have experienced a very rapid development, they are still at an early age that lacks theories to support them. The discipline of Library and Information Science was found to play a very active role in this new area. Future research agenda and directions are also discussed.

scholarly journals De Novo Sequencing and Hybrid Assembly of the Biofuel Crop Jatropha curcas L.: Identification of Quantitative Trait Loci for Geminivirus Resistance

Genes ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 69 ◽  
Author(s):  
Nagesh Kancharla ◽  
Saakshi Jalali ◽  
J. Narasimham ◽  
Vinod Nair ◽  
Vijay Yepuri ◽  
...  
Keyword(s):  
Ssr Markers ◽  
Genome Assembly ◽  
Jatropha Curcas ◽  
Quantitative Trait ◽  
De Novo ◽  
Mapping Population ◽  
Single Copy ◽  
Sequencing Data ◽  
De Novo Genome Assembly ◽  
Sequencing Technologies

Jatropha curcas is an important perennial, drought tolerant plant that has been identified as a potential biodiesel crop. We report here the hybrid de novo genome assembly of J. curcas generated using Illumina and PacBio sequencing technologies, and identification of quantitative loci for Jatropha Mosaic Virus (JMV) resistance. In this study, we generated scaffolds of 265.7 Mbp in length, which correspond to 84.8% of the gene space, using Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis. Additionally, 96.4% of predicted protein-coding genes were captured in RNA sequencing data, which reconfirms the accuracy of the assembled genome. The genome was utilized to identify 12,103 dinucleotide simple sequence repeat (SSR) markers, which were exploited in genetic diversity analysis to identify genetically distinct lines. A total of 207 polymorphic SSR markers were employed to construct a genetic linkage map for JMV resistance, using an interspecific F2 mapping population involving susceptible J. curcas and resistant Jatropha integerrima as parents. Quantitative trait locus (QTL) analysis led to the identification of three minor QTLs for JMV resistance, and the same has been validated in an alternate F2 mapping population. These validated QTLs were utilized in marker-assisted breeding for JMV resistance. Comparative genomics of oil-producing genes across selected oil producing species revealed 27 conserved genes and 2986 orthologous protein clusters in Jatropha. This reference genome assembly gives an insight into the understanding of the complex genetic structure of Jatropha, and serves as source for the development of agronomically improved virus-resistant and oil-producing lines.

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