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July 7, 2019  |  

Complete genome sequences of two geographically distinct Legionella micdadei clinical isolates.

Legionella is a highly diverse genus of intracellular bacterial pathogens that cause Legionnaire’s disease (LD), an often severe form of pneumonia. Two L. micdadei sp. clinical isolates, obtained from patients hospitalized with LD from geographically distinct areas, were sequenced using PacBio SMRT cell technology, identifying incomplete phage regions, which may impact virulence. Copyright © 2017 Osborne et al.


July 7, 2019  |  

Toolkit for automated and rapid discovery of structural variants.

Structural variations (SV) are broadly defined as genomic alterations that affect > 50 bp of DNA, which are shown to have significant effect on evolution and disease. The advent of high throughput sequencing (HTS) technologies and the ability to perform whole genome sequencing (WGS), makes it feasible to study these variants in depth. However, discovery of all forms of SV using WGS has proven to be challenging as the short reads produced by the predominant HTS platforms (<200bp for current technologies) and the fact that most genomes include large amounts of repeats make it very difficult to unambiguously map and accurately characterize such variants. Furthermore, existing tools for SV discovery are primarily developed for only a few of the SV types, which may have conflicting sequence signatures (i.e. read pairs, read depth, split reads) with other, untargeted SV classes. Here we are introduce a new framework, Tardis, which combines multiple read signatures into a single package to characterize most SV types simultaneously, while preventing such conflicts. Tardis also has a modular structure that makes it easy to extend for the discovery of additional forms of SV. Copyright © 2017. Published by Elsevier Inc.


July 7, 2019  |  

No evidence for maintenance of a sympatric Heliconius species barrier by chromosomal inversions.

Mechanisms that suppress recombination are known to help maintain species barriers by preventing the breakup of coadapted gene combinations. The sympatric butterfly species Heliconius melpomene and Heliconius cydno are separated by many strong barriers, but the species still hybridize infrequently in the wild, and around 40% of the genome is influenced by introgression. We tested the hypothesis that genetic barriers between the species are maintained by inversions or other mechanisms that reduce between-species recombination rate. We constructed fine-scale recombination maps for Panamanian populations of both species and their hybrids to directly measure recombination rate within and between species, and generated long sequence reads to detect inversions. We find no evidence for a systematic reduction in recombination rates in F1 hybrids, and also no evidence for inversions longer than 50 kb that might be involved in generating or maintaining species barriers. This suggests that mechanisms leading to global or local reduction in recombination do not play a significant role in the maintenance of species barriers between H. melpomene and H. cydno.


July 7, 2019  |  

Benchmarking computational tools for polymorphic transposable element detection.

Transposable elements (TEs) are an important source of human genetic variation with demonstrable effects on phenotype. Recently, a number of computational methods for the detection of polymorphic TE (polyTE) insertion sites from next-generation sequence data have been developed. The use of such tools will become increasingly important as the pace of human genome sequencing accelerates. For this report, we performed a comparative benchmarking and validation analysis of polyTE detection tools in an effort to inform their selection and use by the TE research community. We analyzed a core set of seven tools with respect to ease of use and accessibility, polyTE detection performance and runtime parameters. An experimentally validated set of 893 human polyTE insertions was used for this purpose, along with a series of simulated data sets that allowed us to assess the impact of sequence coverage on tool performance. The recently developed tool MELT showed the best overall performance followed by Mobster and then RetroSeq. PolyTE detection tools can best detect Alu insertion events in the human genome with reduced reliability for L1 insertions and substantially lowered performance for SVA insertions. We also show evidence that different polyTE detection tools are complementary with respect to their ability to detect a complete set of insertion events. Accordingly, a combined approach, coupled with manual inspection of individual results, may yield the best overall performance. In addition to the benchmarking results, we also provide notes on tool installation and usage as well as suggestions for future polyTE detection algorithm development. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.


July 7, 2019  |  

Complete plastid genomes of the genus Ammopiptanthus and identification of a novel 23-kb rearrangement

Ammopiptanthus is an endangered angiosperm genus with evergreen and broad leaves, grown in the semi-desert regions of eastern Central Asia. We decoded the complete plastid genomes of Ammopiptanthus mongolicus (AM) and Ammopiptanthus nanus (AN), the only two species in the genus Ammopiptanthus. The total length of AM plastome is 153,935 bp, comprising a 83,889-bp long single-copy region (LSC), a 18,022-bp short single-copy region (SSC) and two inverted repeat (IR) regions of 26,012 bp. The total length of AN plastome is 154,140 bp, including a LSC of 84,069 bp, a SSC of 17,971 bp and two IR regions of 26,000 bp, respectively. Each Ammopiptanthus plastome contains 116 unique functional genes, including 78 protein-coding genes, 4 rRNA and 34 tRNA genes. Plastome sequence alignment with other Papilionoid legumes and an outgroup revealed a novel 23-kb inversion between the ndhJ and petN loci in Ammopiptanthus plastomes.


July 7, 2019  |  

Discovery and genotyping of novel sequence insertions in many sequenced individuals

Motivation: Despite recent advances in algorithms design to characterize structural variation using high-throughput short read sequencing (HTS) data, characterization of novel sequence insertions longer than the average read length remains a challenging task. This is mainly due to both computational difficulties and the complexities imposed by genomic repeats in generating reliable assemblies to accurately detect both the sequence content and the exact location of such insertions. Additionally, de novo genome assembly algorithms typically require a very high depth of coverage, which may be a limiting factor for most genome studies. Therefore, characterization of novel sequence insertions is not a routine part of most sequencing projects. There are only a handful of algorithms that are specifically developed for novel sequence insertion discovery that can bypass the need for the whole genome de novo assembly. Still, most such algorithms rely on high depth of coverage, and to our knowledge there is only one method (PopIns) that can use multi-sample data to “collectively” obtain a very high coverage dataset to accurately find insertions common in a given population. Result: Here, we present Pamir, a new algorithm to efficiently and accurately discover and genotype novel sequence insertions using either single or multiple genome sequencing datasets. Pamir is able to detect breakpoint locations of the insertions and calculate their zygosity (i.e. heterozygous versus homozygous) by analyzing multiple sequence signatures, matching one-end-anchored sequences to small-scale de novo assemblies of unmapped reads, and conducting strand-aware local assembly. We test the efficacy of Pamir on both simulated and real data, and demonstrate its potential use in accurate and routine identification of novel sequence insertions in genome projects. Availability and implementation: Pamir is available at https://github.com/vpc-ccg/pamir. Contact:fhach@sfu.ca, prostatecentre.com or calkan@cs.bilkent.edu.tr Supplementary information:Supplementary data are available at Bioinformatics online.


July 7, 2019  |  

Hybrid assembly with long and short reads improves discovery of gene family expansions.

Long-read and short-read sequencing technologies offer competing advantages for eukaryotic genome sequencing projects. Combinations of both may be appropriate for surveys of within-species genomic variation.We developed a hybrid assembly pipeline called “Alpaca” that can operate on 20X long-read coverage plus about 50X short-insert and 50X long-insert short-read coverage. To preclude collapse of tandem repeats, Alpaca relies on base-call-corrected long reads for contig formation.Compared to two other assembly protocols, Alpaca demonstrated the most reference agreement and repeat capture on the rice genome. On three accessions of the model legume Medicago truncatula, Alpaca generated the most agreement to a conspecific reference and predicted tandemly repeated genes absent from the other assemblies.Our results suggest Alpaca is a useful tool for investigating structural and copy number variation within de novo assemblies of sampled populations.


July 7, 2019  |  

CLOVE: classification of genomic fusions into structural variation events.

A precise understanding of structural variants (SVs) in DNA is important in the study of cancer and population diversity. Many methods have been designed to identify SVs from DNA sequencing data. However, the problem remains challenging because existing approaches suffer from low sensitivity, precision, and positional accuracy. Furthermore, many existing tools only identify breakpoints, and so not collect related breakpoints and classify them as a particular type of SV. Due to the rapidly increasing usage of high throughput sequencing technologies in this area, there is an urgent need for algorithms that can accurately classify complex genomic rearrangements (involving more than one breakpoint or fusion).We present CLOVE, an algorithm for integrating the results of multiple breakpoint or SV callers and classifying the results as a particular SV. CLOVE is based on a graph data structure that is created from the breakpoint information. The algorithm looks for patterns in the graph that are characteristic of more complex rearrangement types. CLOVE is able to integrate the results of multiple callers, producing a consensus call.We demonstrate using simulated and real data that re-classified SV calls produced by CLOVE improve on the raw call set of existing SV algorithms, particularly in terms of accuracy. CLOVE is freely available from http://www.github.com/PapenfussLab .


July 7, 2019  |  

Critical points for an accurate human genome analysis.

Next-generation sequencing is radically changing how DNA diagnostic laboratories operate. What started as a single-gene profession is now developing into gene panel sequencing and whole-exome and whole-genome sequencing (WES/WGS) analyses. With further advances in sequencing technology and concomitant price reductions, WGS will soon become the standard and be routinely offered. Here, we focus on the critical steps involved in performing WGS, with a particular emphasis on points where WGS differs from WES, the important variables that should be taken into account, and the quality control measures that can be taken to monitor the process. The points discussed here, combined with recent publications on guidelines for reporting variants, will facilitate the routine implementation of WGS into a diagnostic setting.© 2017 Wiley Periodicals, Inc.


July 7, 2019  |  

Automated structural variant verification in human genomesw using single-molecule electronic DNA mapping.

The importance of structural variation in human disease and the difficulty of detecting structural variants larger than 50 base pairs has led to the development of several long-read sequencing technologies and optical mapping platforms. Frequently, multiple technologies and ad hoc methods are required to obtain a consensus regarding the location, size and nature of a structural variant, with no approach able to reliably bridge the gap of variant sizes between the domain of short-read approaches and the largest rearrangements observed with optical mapping. To address this unmet need, we have developed a new software package, SV-VerifyTM, which utilizes data collected with the Nabsys High Definition Mapping (HD-MappingTM) system, to perform hypothesis-based verification of putative deletions. We demonstrate that whole genome maps, constructed from electronic detection of tagged DNA, hundreds of kilobases in length, can be used effectively to facilitate calling of structural variants ranging in size from 300 base pairs to hundreds of kilobase pairs. SV-Verify implements hypothesis-based verification of putative structural variants using a set of support vector machines and is capable of concurrently testing several thousand independent hypotheses. We describe support vector machine training, utilizing a well-characterized human genome, and application of the resulting classifiers to another human genome, demonstrating high sensitivity and specificity for deletions >= 300 base pairs.


July 7, 2019  |  

Resolving multicopy duplications de novo using polyploid phasing

While the rise of single-molecule sequencing systems has enabled an unprecedented rise in the ability to assemble complex regions of the genome, long segmental duplications in the genome still remain a challenging frontier in assembly. Segmental duplications are at the same time both gene rich and prone to large structural rearrangements, making the resolution of their sequences important in medical and evolutionary studies. Duplicated sequences that are collapsed in mammalian de novo assemblies are rarely identical; after a sequence is duplicated, it begins to acquire paralog-specific variants. In this paper, we study the problem of resolving the variations in multicopy, long segmental duplications by developing and utilizing algorithms for polyploid phasing. We develop two algorithms: the first one is targeted at maximizing the likelihood of observing the reads given the underlying haplotypes using discrete matrix completion. The second algorithm is based on correlation clustering and exploits an assumption, which is often satisfied in these duplications, that each paralog has a sizable number of paralog-specific variants. We develop a detailed simulation methodology and demonstrate the superior performance of the proposed algorithms on an array of simulated datasets. We measure the likelihood score as well as reconstruction accuracy, i.e., what fraction of the reads are clustered correctly. In both the performance metrics, we find that our algorithms dominate existing algorithms on more than 93% of the datasets. While the discrete matrix completion performs better on likelihood score, the correlation-clustering algorithm performs better on reconstruction accuracy due to the stronger regularization inherent in the algorithm. We also show that our correlation-clustering algorithm can reconstruct on average 7.0 haplotypes in 10-copy duplication datasets whereas existing algorithms reconstruct less than one copy on average.


July 7, 2019  |  

XCAVATOR: accurate detection and genotyping of copy number variants from second and third generation whole-genome sequencing experiments.

We developed a novel software package, XCAVATOR, for the identification of genomic regions involved in copy number variants/alterations (CNVs/CNAs) from short and long reads whole-genome sequencing experiments.By using simulated and real datasets we showed that our tool, based on read count approach, is capable to predict the boundaries and the absolute number of DNA copies CNVs/CNAs with high resolutions. To demonstrate the power of our software we applied it to the analysis Illumina and Pacific Bioscencies data and we compared its performance to other ten state of the art tools.All the analyses we performed demonstrate that XCAVATOR is capable to detect germline and somatic CNVs/CNAs outperforming all the other tools we compared. XCAVATOR is freely available at http://sourceforge.net/projects/xcavator/ .


July 7, 2019  |  

SVachra: a tool to identify genomic structural variation in mate pair sequencing data containing inward and outward facing reads.

Characterization of genomic structural variation (SV) is essential to expanding the research and clinical applications of genome sequencing. Reliance upon short DNA fragment paired end sequencing has yielded a wealth of single nucleotide variants and internal sequencing read insertions-deletions, at the cost of limited SV detection. Multi-kilobase DNA fragment mate pair sequencing has supplemented the void in SV detection, but introduced new analytic challenges requiring SV detection tools specifically designed for mate pair sequencing data. Here, we introduce SVachra – Structural Variation Assessment of CHRomosomal Aberrations, a breakpoint calling program that identifies large insertions-deletions, inversions, inter- and intra-chromosomal translocations utilizing both inward and outward facing read types generated by mate pair sequencing.We demonstrate SVachra’s utility by executing the program on large-insert (Illumina Nextera) mate pair sequencing data from the personal genome of a single subject (HS1011). An additional data set of long-read (Pacific BioSciences RSII) was also generated to validate SV calls from SVachra and other comparison SV calling programs. SVachra exhibited the highest validation rate and reported the widest distribution of SV types and size ranges when compared to other SV callers.SVachra is a highly specific breakpoint calling program that exhibits a more unbiased SV detection methodology than other callers.


July 7, 2019  |  

The Mobile Element Locator Tool (MELT): population-scale mobile element discovery and biology.

Mobile element insertions (MEIs) represent ~25% of all structural variants in human genomes. Moreover, when they disrupt genes, MEIs can influence human traits and diseases. Therefore, MEIs should be fully discovered along with other forms of genetic variation in whole genome sequencing (WGS) projects involving population genetics, human diseases, and clinical genomics. Here, we describe the Mobile Element Locator Tool (MELT), which was developed as part of the 1000 Genomes Project to perform MEI discovery on a population scale. Using both Illumina WGS data and simulations, we demonstrate that MELT outperforms existing MEI discovery tools in terms of speed, scalability, specificity, and sensitivity, while also detecting a broader spectrum of MEI-associated features. Several run modes were developed to perform MEI discovery on local and cloud systems. In addition to using MELT to discover MEIs in modern humans as part of the 1000 Genomes Project, we also used it to discover MEIs in chimpanzees and ancient (Neanderthal and Denisovan) hominids. We detected diverse patterns of MEI stratification across these populations that likely were caused by (1) diverse rates of MEI production from source elements, (2) diverse patterns of MEI inheritance, and (3) the introgression of ancient MEIs into modern human genomes. Overall, our study provides the most comprehensive map of MEIs to date spanning chimpanzees, ancient hominids, and modern humans and reveals new aspects of MEI biology in these lineages. We also demonstrate that MELT is a robust platform for MEI discovery and analysis in a variety of experimental settings.© 2017 Gardner et al.; Published by Cold Spring Harbor Laboratory Press.


July 7, 2019  |  

Structural variation offers new home for disease associations and gene discovery

Following completion of the Human Genome Project, most studies of human genetic variation have centered on single nucleotide polymorphisms (SNPs). SNPs are numerous in individual genomes and serve as useful genetic markers in association studies across a population. These markers have been leveraged to identify genetic loci for disease risk and draw associations with numerous traits of interest. Despite their usefulness, SNPs do not tell the whole story. For example, most SNPs are associated with only a small increased risk of disease, and they usually cannot identify on their own which genes are causal. This has resulted in what many researchers have referred to as missing or hidden heritability.


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