German-wide interlaboratory study compares consistency, accuracy and reproducibility of whole-genome short read sequencing

AbstractWe compared the consistency, accuracy and reproducibility of next-generation short read sequencing between ten laboratories involved in food safety (research institutes, state laboratories, universities and companies) from Germany and Austria. Participants were asked to sequence six DNA samples of three bacterial species (Campylobacter jejuni, Listeria monocytogenes and Salmonella enterica) in duplicate, according to their routine in-house sequencing protocol. Four different types of Illumina sequencing platforms (MiSeq, NextSeq, iSeq, NovaSeq) and one Ion Torrent sequencing instrument (S5) were involved in the study. Sequence quality parameters were determined for all data sets and centrally compared between laboratories. SNP / and cgMLST calling were performed to assess the reproducibility of sequence data collected for individual samples. Overall, we found Illumina short read data to be more accurate and consistent than Ion Torrent sequence data, with little variation between the different Illumina instruments. Two laboratories with Illumina instruments submitted sequence data with lower quality, probably due to the use of a library preparation kit, which shows difficulty in sequencing low GC genome regions. Differences in data quality were more evident after assembling short reads into genome assemblies, with Ion Torrent assemblies featuring a great number of allele differences to Illumina assemblies. Clonality of samples was confirmed through SNP calling, which proved to be a more suitable method for an integrated data analysis of Illumina and Ion Torrent data sets, than cgMLST calling.

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REscan: inferring repeat expansions and structural variation in paired-end short read sequencing data

Bioinformatics ◽

10.1093/bioinformatics/btaa753 ◽

2020 ◽

Author(s):

Russell Lewis McLaughlin

Keyword(s):

Structural Variation ◽

Sequence Data ◽

Neurological Diseases ◽

Repeat Expansion ◽

Sequencing Data ◽

Short Read ◽

Short Read Sequencing ◽

Repeat Expansions ◽

Paired End Sequencing

Abstract Motivation Repeat expansions are an important class of genetic variation in neurological diseases. However, the identification of novel repeat expansions using conventional sequencing methods is a challenge due to their typical lengths relative to short sequence reads and difficulty in producing accurate and unique alignments for repetitive sequence. However, this latter property can be harnessed in paired-end sequencing data to infer the possible locations of repeat expansions and other structural variation. Results This article presents REscan, a command-line utility that infers repeat expansion loci from paired-end short read sequencing data by reporting the proportion of reads orientated towards a locus that do not have an adequately mapped mate. A high REscan statistic relative to a population of data suggests a repeat expansion locus for experimental follow-up. This approach is validated using genome sequence data for 259 cases of amyotrophic lateral sclerosis, of which 24 are positive for a large repeat expansion in C9orf72, showing that REscan statistics readily discriminate repeat expansion carriers from non-carriers. Availabilityand implementation C source code at https://github.com/rlmcl/rescan (GNU General Public Licence v3).

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Rapid Mycobacterium tuberculosis spoligotyping from uncorrected long reads using Galru

10.1101/2020.05.31.126490 ◽

2020 ◽

Author(s):

Andrew J. Page ◽

Nabil-Fareed Alikhan ◽

Michael Strinden ◽

Thanh Le Viet ◽

Timofey Skvortsov

Keyword(s):

Mycobacterium Tuberculosis ◽

State Of The Art ◽

Sequence Data ◽

Human Pathogen ◽

Sequencing Data ◽

Short Read ◽

Short Read Sequencing ◽

Long Reads ◽

Long Read

AbstractSpoligotyping of Mycobacterium tuberculosis provides a subspecies classification of this major human pathogen. Spoligotypes can be predicted from short read genome sequencing data; however, no methods exist for long read sequence data such as from Nanopore or PacBio. We present a novel software package Galru, which can rapidly detect the spoligotype of a Mycobacterium tuberculosis sample from as little as a single uncorrected long read. It allows for near real-time spoligotyping from long read data as it is being sequenced, giving rapid sample typing. We compare it to the existing state of the art software and find it performs identically to the results obtained from short read sequencing data. Galru is freely available from https://github.com/quadram-institute-bioscience/galru under the GPLv3 open source licence.

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Fast and accurate HLA typing from short-read next-generation sequence data with xHLA

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1707945114 ◽

2017 ◽

Vol 114 (30) ◽

pp. 8059-8064 ◽

Cited By ~ 54

Author(s):

Chao Xie ◽

Zhen Xuan Yeo ◽

Marie Wong ◽

Jason Piper ◽

Tao Long ◽

...

Keyword(s):

Sequence Data ◽

Sequence Similarity ◽

Amino Acid Level ◽

Hla Typing ◽

Sequencing Data ◽

Desktop Computer ◽

Short Read ◽

Short Read Sequencing ◽

Hla Genes ◽

Human Chromosome 6

The HLA gene complex on human chromosome 6 is one of the most polymorphic regions in the human genome and contributes in large part to the diversity of the immune system. Accurate typing of HLA genes with short-read sequencing data has historically been difficult due to the sequence similarity between the polymorphic alleles. Here, we introduce an algorithm, xHLA, that iteratively refines the mapping results at the amino acid level to achieve 99–100% four-digit typing accuracy for both class I and II HLA genes, taking only∼3 min to process a 30× whole-genome BAM file on a desktop computer.

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Consistent, comprehensive and computationally efficient OTU definitions

10.7287/peerj.preprints.411 ◽

2014 ◽

Author(s):

Jai Ram Rideout ◽

Yan He ◽

Jose Antonio Navas-Molina ◽

William A Walters ◽

Luke K Ursell ◽

...

Keyword(s):

16S Rrna ◽

De Novo ◽

Sequence Data ◽

Marker Gene ◽

Community Analysis ◽

Microbial Community Analysis ◽

Reference Database ◽

Data Sets ◽

Computationally Efficient ◽

Sequencing Platforms

We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to “classic” open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, “classic” open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of “classic” open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by “classic” open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME’s uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME’s OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.

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MOST: a modified MLST typing tool based on short read sequencing

PeerJ ◽

10.7717/peerj.2308 ◽

2016 ◽

Vol 4 ◽

pp. e2308 ◽

Cited By ~ 63

Author(s):

Rediat Tewolde ◽

Timothy Dallman ◽

Ulf Schaefer ◽

Carmen L. Sheppard ◽

Philip Ashton ◽

...

Keyword(s):

Conventional Method ◽

Sequence Data ◽

Pcr Amplification ◽

Housekeeping Genes ◽

Data Sets ◽

Bacterial Genomes ◽

Bacterial Populations ◽

Short Read ◽

Short Read Sequence ◽

Low Coverage

Multilocus sequence typing (MLST) is an effective method to describe bacterial populations. Conventionally, MLST involves Polymerase Chain Reaction (PCR) amplification of housekeeping genes followed by Sanger DNA sequencing. Public Health England (PHE) is in the process of replacing the conventional MLST methodology with a method based on short read sequence data derived from Whole Genome Sequencing (WGS). This paper reports the comparison of the reliability of MLST results derived from WGS data, comparing mapping and assembly-based approaches to conventional methods using 323 bacterial genomes of diverse species. The sensitivity of the two WGS based methods were further investigated with 26 mixed and 29 low coverage genomic data sets fromSalmonella enteridisandStreptococcus pneumoniae. Of the 323 samples, 92.9% (n= 300), 97.5% (n= 315) and 99.7% (n= 322) full MLST profiles were derived by the conventional method, assembly- and mapping-based approaches, respectively. The concordance between samples that were typed by conventional (92.9%) and both WGS methods was 100%. From the 55 mixed and low coverage genomes, 89.1% (n= 49) and 67.3% (n= 37) full MLST profiles were derived from the mapping and assembly based approaches, respectively. In conclusion, deriving MLST from WGS data is more sensitive than the conventional method. When comparing WGS based methods, the mapping based approach was the most sensitive. In addition, the mapping based approach described here derives quality metrics, which are difficult to determine quantitatively using conventional and WGS-assembly based approaches.

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Using Short Read Sequencing to Characterise Balanced Reciprocal Translocations in Pigs

10.21203/rs.3.rs-28830/v1 ◽

2020 ◽

Author(s):

Aniek Cornelia Bouwman ◽

Martijn F.L. Derks ◽

Marleen L.W.J. Broekhuijse ◽

Barbara Harlizius ◽

Roel F. Veerkamp

Keyword(s):

Sequence Data ◽

Variant Calling ◽

Reciprocal Translocations ◽

Short Read ◽

Short Read Sequencing ◽

Long Read ◽

Short Read Sequence ◽

Staining Techniques ◽

Chromosome Staining ◽

Paired End Sequencing

Abstract Background A balanced constitutional reciprocal translocation (RT) is a mutual exchange of terminal segments of two non-homologous chromosomes without any loss or gain of DNA in germline cells. Carriers of balanced RTs are viable individuals with no apparent phenotypical consequences. These animals produce, however, unbalanced gametes and show therefore reduced fertility and offspring with congenital abnormalities. This cytogenetic abnormality is usually detected using chromosome staining techniques. The aim of this study was to test the possibilities of using paired end short read sequencing for detection of balanced RTs in boars and investigate their breakpoints and junctions.Results Balanced RTs were recovered in a blinded analysis, using structural variant calling software DELLY, in 6 of the 7 carriers with 30 fold short read paired end sequencing. In 15 non-carriers we did not detect any RTs. Reducing the coverage to 20 fold, 15 fold and 10 fold showed that at least 20 fold coverage is required to obtain good results. One RT was not detected using the blind screening, however, a highly likely RT was discovered after unblinding. This RT was located in a repetitive region, showing the limitations of short read sequence data. The detailed analysis of the breakpoints and junctions suggested three junctions showing microhomology, three junctions with blunt-end ligation, and three micro-insertions at the breakpoint junctions. The RTs detected also showed to disrupt genes.Conclusions We conclude that paired end short read sequence data can be used to detect and characterize balanced reciprocal translocations, if sequencing depth is at least 20 fold coverage. However, translocations in repetitive areas may require large fragments or even long read sequence data.

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Rapid, Paralog-Sensitive CNV Analysis of 2457 Human Genomes Using QuicK-mer2

Genes ◽

10.3390/genes11020141 ◽

2020 ◽

Vol 11 (2) ◽

pp. 141 ◽

Cited By ~ 5

Author(s):

Feichen Shen ◽

Jeffrey M. Kidd

Keyword(s):

Copy Number Variation ◽

Copy Number ◽

Sequence Data ◽

Data Sets ◽

Short Read ◽

Major Mechanism ◽

Rapid Construction ◽

A Genome ◽

Number Variation ◽

Short Read Sequence

Gene duplication is a major mechanism for the evolution of gene novelty, and copy-number variation makes a major contribution to inter-individual genetic diversity. However, most approaches for studying copy-number variation rely upon uniquely mapping reads to a genome reference and are unable to distinguish among duplicated sequences. Specialized approaches to interrogate specific paralogs are comparatively slow and have a high degree of computational complexity, limiting their effective application to emerging population-scale data sets. We present QuicK-mer2, a self-contained, mapping-free approach that enables the rapid construction of paralog-specific copy-number maps from short-read sequence data. This approach is based on the tabulation of unique k-mer sequences from short-read data sets, and is able to analyze a 20X coverage human genome in approximately 20 min. We applied our approach to newly released sequence data from the 1000 Genomes Project, constructed paralog-specific copy-number maps from 2457 unrelated individuals, and uncovered copy-number variation of paralogous genes. We identify nine genes where none of the analyzed samples have a copy number of two, 92 genes where the majority of samples have a copy number other than two, and describe rare copy number variation effecting multiple genes at the APOBEC3 locus.

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FastqPuri: high-performance preprocessing of RNA-seq data

10.1101/480707 ◽

2018 ◽

Author(s):

Paula Pérez-Rubio ◽

Claudio Lottaz ◽

Julia C Engelmann

Keyword(s):

High Performance ◽

Sequence Data ◽

Transcript Expression ◽

Memory Usage ◽

Rna Seq ◽

Sequencing Data ◽

Total Size ◽

Short Read ◽

Quality Filtering ◽

Sequence Quality

AbstractBackgroundRNA sequencing (RNA-seq) has become the standard means of analyzing gene and transcript expression in high-throughput. While previously sequence alignment was a time demanding step, fast alignment methods and even more so transcript counting methods which avoid mapping and quantify gene and transcript expression by evaluating whether a read is compatible with a transcript, have led to significant speed-ups in data analysis. Now, the most time demanding step in the analysis of RNA-seq data is preprocessing the raw sequence data, such as running quality control and adapter, contamination and quality filtering before transcript or gene quantification. To do so, many researchers chain different tools, but a comprehensive, flexible and fast software that covers all preprocessing steps is currently missing.ResultsWe here present FastqPuri, a light-weight and highly efficient preprocessing tool for fastq data. FastqPuri provides sequence quality reports on the sample and dataset level with new plots which facilitate decision making for subsequent quality filtering. Moreover, FastqPuri efficiently removes adapter sequences and sequences from biological contamination from the data. It accepts both single- and paired-end data in uncompressed or compressed fastq files. FastqPuri can be run stand-alone and is suitable to be run within pipelines. We benchmarked FastqPuri against existing tools and found that FastqPuri is superior in terms of speed, memory usage, versatility and comprehensiveness. Conclusions: FastqPuri is a new tool which covers all aspects of short read sequence data preprocessing. It was designed for RNA-seq data to meet the needs for fast preprocessing of fastq data to allow transcript and gene counting, but it is suitable to process any short read sequencing data of which high sequence quality is needed, such as for genome assembly or SNV (single nucleotide variant) detection. FastqPuri is most flexible in filtering undesired biological sequences by offering two approaches to optimize speed and memory usage dependent on the total size of the potential contaminating sequences. FastqPuri is available at https://github.com/jengelmann/FastqPuri. It is implemented in C and R and licensed under GPL v3.

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Benchmarking of long-read assemblers for prokaryote whole genome sequencing

F1000Research ◽

10.12688/f1000research.21782.1 ◽

2019 ◽

Vol 8 ◽

pp. 2138 ◽

Cited By ~ 15

Author(s):

Ryan R. Wick ◽

Kathryn E. Holt

Keyword(s):

Data Sets ◽

Computationally Efficient ◽

Short Read Sequencing ◽

Oxford Nanopore ◽

Long Read ◽

Sequencing Platforms ◽

Computational Resources ◽

Assembly Algorithms ◽

Oxford Nanopore Technologies ◽

Multiple Assembly

Background: Data sets from long-read sequencing platforms (Oxford Nanopore Technologies and Pacific Biosciences) allow for most prokaryote genomes to be completely assembled – one contig per chromosome or plasmid. However, the high per-read error rate of long-read sequencing necessitates different approaches to assembly than those used for short-read sequencing. Multiple assembly tools (assemblers) exist, which use a variety of algorithms for long-read assembly. Methods: We used 500 simulated read sets and 120 real read sets to assess the performance of six long-read assemblers (Canu, Flye, Miniasm/Minipolish, Raven, Redbean and Shasta) across a wide variety of genomes and read parameters. Assemblies were assessed on their structural accuracy/completeness, sequence identity, contig circularisation and computational resources used. Results: Canu v1.9 produced moderately reliable assemblies but had the longest runtimes of all assemblers tested. Flye v2.6 was more reliable and did particularly well with plasmid assembly. Miniasm/Minipolish v0.3 was the only assembler which consistently produced clean contig circularisation. Raven v0.0.5 was the most reliable for chromosome assembly, though it did not perform well on small plasmids and had circularisation issues. Redbean v2.5 and Shasta v0.3.0 were computationally efficient but more likely to produce incomplete assemblies. Conclusions: Of the assemblers tested, Flye, Miniasm/Minipolish and Raven performed best overall. However, no single tool performed well on all metrics, highlighting the need for continued development on long-read assembly algorithms.

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MOST: A modified MLST typing tool based on short read sequencing

10.7287/peerj.preprints.1971v1 ◽

2016 ◽

Author(s):

Rediat Tewolde ◽

Timothy Dallman ◽

Ulf Schaefer ◽

Carmen L Sheppard ◽

Philip Ashton ◽

...

Keyword(s):

Conventional Method ◽

Sequence Data ◽

Pcr Amplification ◽

Housekeeping Genes ◽

Data Sets ◽

Bacterial Genomes ◽

Bacterial Populations ◽

Short Read ◽

Short Read Sequence ◽

Low Coverage

Multilocus sequence typing (MLST) is an effective method to describe bacterial populations. Conventionally, MLST involves Polymerase Chain Reaction (PCR)amplification of housekeeping genes followed by Sanger DNA sequencing. Public Health England (PHE) is in the process of replacing the conventional MLST methodology with a method based on short read sequence data derived from Whole Genome Sequencing (WGS). This paper reports the comparison of the reliability of MLST results derived from WGS data, comparing mapping and assembly-based approaches to conventional methods using 325 bacterial genomes of diverse species. The sensitivity of the two WGS based methods were further investigated with 26 mixed and 29 low coverage genomic data sets from Salmonella enteridis and Streptococcus pneumoniae. Of the 325 samples, 92.9% (n=302), 97.2% (n=316) and 99.7% (n=324) full MLST profiles were derived by the conventional method, assembly- and mapping-based approaches, respectively. The concordance between samples that were typed by conventional (92.9%) and both WGS methods was 100%. From the 55 mixed and low coverage genomes, 90.9% (n=50) and 67.3% (n=37) full MLST profiles were derived from the mapping and assembly based approaches, respectively. In conclusion, deriving MLST from WGS data is more sensitive than the conventional method. When comparing WGS based methods, the mapping based approach was the most sensitive. In addition, the mapping based approach described here derives quality metrics, which are difficult to determine quantitatively using conventional and WGS-assembly based approaches.

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