Mapping-based Approaches Research Articles

Genomic epidemiology and longitudinal sampling of ward wastewater environments and patients reveals complexity of the transmission dynamics of bla KPC-carbapenemase-producing Enterobacterales in a hospital setting.

Healthcare-associated wastewater and asymptomatic patient reservoirs colonized by carbapenemase-producing Enterobacterales (CPE) contribute to nosocomial CPE dissemination, but the characteristics and dynamics of this remain unclear. We systematically sampled wastewater sites (n = 4488 samples; 349 sites) and patients (n = 1247) across six wards over 6-12 months to understand blaKPC-associated CPE (KPC-E) diversity within these reservoirs and transmission in a healthcare setting. Up to five KPC-E-positive isolates per sample were sequenced (Illumina). Recombination-adjusted phylogenies were used to define genetically related strains; assembly and mapping-based approaches were used to characterize antimicrobial resistance genes, insertion sequences (ISs) and Tn4401 types/target site sequences. The accessory genome was evaluated in some of the largest clusters, and those crossing reservoirs. Wastewater site KPC-E-positivity was substantial [101/349 sites (28.9%); 228/5601 (4.1%) patients cultured]. Thirteen KPC-E species and 109 strains were identified using genomics, and 24% of wastewater and 26% of patient KPC-E-positive samples harboured one or more strains. Most diversity was explained by the individual niche, suggesting localized factors are important in selection and spread. Tn4401 + flanking target site sequence diversity was greater in wastewater sites (P < 0.001), which might favour Tn4401-associated transposition/evolution. Shower/bath- and sluice/mop-associated sites were more likely to be KPC-E-positive (adjusted OR = 2.69; 95% CI: 1.44-5.01; P = 0.0019; and adjusted OR = 2.60; 95% CI: 1.04-6.52; P = 0.0410, respectively). Different strains had different blaKPC dissemination dynamics. We identified substantial and diverse KPC-E colonization of wastewater sites and patients in this hospital setting. Reservoir and niche-specific factors (e.g. microbial interactions, selection pressures), and different strains and mobile genetic elements likely affect transmission dynamics. This should be considered in surveillance and control strategies.

TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches

Transposable Element MOnitoring with LOng-reads (TrEMOLO) is a new software that combines assembly- and mapping-based approaches to robustly detect genetic elements called transposable elements (TEs). Using high- or low-quality genome assemblies, TrEMOLO can detect most TE insertions and deletions and estimate their allele frequency in populations. Benchmarking with simulated data revealed that TrEMOLO outperforms other state-of-the-art computational tools. TE detection and frequency estimation by TrEMOLO were validated using simulated and experimental datasets. Therefore, TrEMOLO is a comprehensive and suitable tool to accurately study TE dynamics. TrEMOLO is available under GNU GPL3.0 at https://github.com/DrosophilaGenomeEvolution/TrEMOLO.

Pangenome-based genome inference allows efficient and accurate genotyping across a wide spectrum of variant classes

Typical genotyping workflows map reads to a reference genome before identifying genetic variants. Generating such alignments introduces reference biases and comes with substantial computational burden. Furthermore, short-read lengths limit the ability to characterize repetitive genomic regions, which are particularly challenging for fast k-mer-based genotypers. In the present study, we propose a new algorithm, PanGenie, that leverages a haplotype-resolved pangenome reference together with k-mer counts from short-read sequencing data to genotype a wide spectrum of genetic variation—a process we refer to as genome inference. Compared with mapping-based approaches, PanGenie is more than 4 times faster at 30-fold coverage and achieves better genotype concordances for almost all variant types and coverages tested. Improvements are especially pronounced for large insertions (≥50 bp) and variants in repetitive regions, enabling the inclusion of these classes of variants in genome-wide association studies. PanGenie efficiently leverages the increasing amount of haplotype-resolved assemblies to unravel the functional impact of previously inaccessible variants while being faster compared with alignment-based workflows.

Subpixel Change Detection Based on Improved Abundance Values for Remote Sensing Images

To achieve land cover change detection (LCCD) with both fine spatial and temporal resolutions from remote sensing images, subpixel mapping-based approaches have been widely studied in recent years. The fine spatial but coarse temporal resolution image and the coarse spatial but fine temporal image are used to accomplish LCCD by combining their advantages. However, the performance of subpixel mapping is easily affected by the accuracy of spectral unmixing, thereby reducing the reliability of LCCD. In this paper, a novel subpixel change detection scheme based on improved abundance values is proposed to tackle the aforementioned problem, in which the spatial distribution of fine spatial resolution image is borrowed to promote the accuracy of spectral unmixing. First, the coarse spatial resolution image is used to generate the original abundance image by the spectral unmixing method. Second, the spatial distribution information of the fine spatial resolution image is incorporated into the original abundance image to obtain improved abundance values. Third, the fine spatial resolution subpixel map can be generated by the subpixel mapping method using the improved abundance values. At last, the fine resolution change map can be obtained by comparing the subpixel map with the fine spatial resolution image. Experiments are conducted on a simulated dataset based on Landsat-7 images and two real datasets based on Landsat-8 and MODIS images. The results of the real datasets showed that the proposed method can effectively improve the performance of LCCD with an overall accuracy of approximately 1.26% and 0.79% to the existing methods.

Nebula: ultra-efficient mapping-free structural variant genotyper.

Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

TRiCoLOR: tandem repeat profiling using whole-genome long-read sequencing data.

BackgroundTandem repeat sequences are widespread in the human genome, and their expansions cause multiple repeat-mediated disorders. Genome-wide discovery approaches are needed to fully elucidate their roles in health and disease, but resolving tandem repeat variation accurately remains a challenging task. While traditional mapping-based approaches using short-read data have severe limitations in the size and type of tandem repeats they can resolve, recent third-generation sequencing technologies exhibit substantially higher sequencing error rates, which complicates repeat resolution.ResultsWe developed TRiCoLOR, a freely available tool for tandem repeat profiling using error-prone long reads from third-generation sequencing technologies. The method can identify repetitive regions in sequencing data without a prior knowledge of their motifs or locations and resolve repeat multiplicity and period size in a haplotype-specific manner. The tool includes methods to interactively visualize the identified repeats and to trace their Mendelian consistency in pedigrees.ConclusionsTRiCoLOR demonstrates excellent performance and improved sensitivity and specificity compared with alternative tools on synthetic data. For real human whole-genome sequencing data, TRiCoLOR achieves high validation rates, suggesting its suitability to identify tandem repeat variation in personal genomes.

Functional and Comparative Genomic Analysis of Integrated Prophage-Like Sequences in "Candidatus Liberibacter asiaticus".

Huanglongbing disease (HLB; yellow shoot disease) is a severe worldwide infectious disease for citrus family plants. The pathogen "Candidatus Liberibacter asiaticus" is an alphaproteobacterium of the Rhizobiaceae family that has been identified as the causative agent of HLB. The virulence of "Ca. Liberibacter asiaticus" has been attributed, in part, to prophage-carried genes. Prophage and prophage-like elements have been identified in 12 of the 15 available "Ca. Liberibacter asiaticus" genomes and are classified into three prophage types. Here, we reexamined all 15 "Ca. Liberibacter asiaticus" genomes using a de novo prediction approach and expanded the number of prophage-like elements from 16 to 33. Further, we found that all of the "Ca. Liberibacter asiaticus" genomes contained at least one prophage-like sequence. Comparative analysis revealed a prevalent, albeit previously unknown, prophage-like sequence type that is a remnant of an integrated prophage. Notably, this remnant prophage is found in the Ishi-1 "Ca. Liberibacter asiaticus" strain that had previously been reported as lacking prophages. Our findings provide both a resource for data and new insights into the evolutionary relationship between phage and "Ca. Liberibacter asiaticus" pathogenicity.IMPORTANCE Huanglongbing (HLB) disease is threatening citrus production worldwide. The causative agent is "Candidatus Liberibacter asiaticus." Prior work using mapping-based approaches identified prophage-like sequences in some "Ca. Liberibacter asiaticus" genomes but not all. Here, we utilized a de novo approach that expands the number of prophage-like elements found in "Ca. Liberibacter asiaticus" from 16 to 33 and identified at least one prophage-like sequence in all "Ca. Liberibacter asiaticus" strains. Furthermore, we identified a prophage-like sequence type that is a remnant of an integrated prophage-expanding the number of prophage types in "Ca. Liberibacter asiaticus" from 3 to 4. Overall, the findings will help researchers investigate the role of prophage in the ecology, evolution, and pathogenicity of "Ca. Liberibacter asiaticus."

Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants

SummaryDe novo genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both de novo single-nucleotide variants and indels with high accuracy.

Deep learning for minimum mean-square error approaches to speech enhancement

Recently, the focus of speech enhancement research has shifted from minimum mean-square error (MMSE) approaches, like the MMSE short-time spectral amplitude (MMSE-STSA) estimator, to state-of-the-art masking- and mapping-based deep learning approaches. We aim to bridge the gap between these two differing speech enhancement approaches. Deep learning methods for MMSE approaches are investigated in this work, with the objective of producing intelligible enhanced speech at a high quality. Since the speech enhancement performance of an MMSE approach improves with the accuracy of the used a priori signal-to-noise ratio (SNR) estimator, a residual long short-term memory (ResLSTM) network is utilised here to accurately estimate the a priori SNR. MMSE approaches utilising the ResLSTM a priori SNR estimator are evaluated using subjective and objective measures of speech quality and intelligibility. The tested conditions include real-world non-stationary and coloured noise sources at multiple SNR levels. MMSE approaches utilising the proposed a priori SNR estimator are able to achieve higher enhanced speech quality and intelligibility scores than recent masking- and mapping-based deep learning approaches. The results presented in this work show that the performance of an MMSE approach to speech enhancement significantly increases when utilising deep learning.Availability: The proposed a priori SNR estimator is available at: https://github.com/anicolson/DeepXi.

DREAM-Yara: an exact read mapper for very large databases with short update time.

Mapping-based approaches have become limited in their application to very large sets of references since computing an FM-index for very large databases (e.g. >10 GB) has become a bottleneck. This affects many analyses that need such index as an essential step for approximate matching of the NGS reads to reference databases. For instance, in typical metagenomics analysis, the size of the reference sequences has become prohibitive to compute a single full-text index on standard machines. Even on large memory machines, computing such index takes about 1 day of computing time. As a result, updates of indices are rarely performed. Hence, it is desirable to create an alternative way of indexing while preserving fast search times. To solve the index construction and update problem we propose the DREAM (Dynamic seaRchablE pArallel coMpressed index) framework and provide an implementation. The main contributions are the introduction of an approximate search distributor via a novel use of Bloom filters. We combine several Bloom filters to form an interleaved Bloom filter and use this new data structure to quickly exclude reads for parts of the databases where they cannot match. This allows us to keep the databases in several indices which can be easily rebuilt if parts are updated while maintaining a fast search time. The second main contribution is an implementation of DREAM-Yara a distributed version of a fully sensitive read mapper under the DREAM framework. https://gitlab.com/pirovc/dream_yara/.

DACCOR-Detection, characterization, and reconstruction of repetitive regions in bacterial genomes.

The reconstruction of genomes using mapping-based approaches with short reads experiences difficulties when resolving repetitive regions. These repetitive regions in genomes result in low mapping qualities of the respective reads, which in turn lead to many unresolved bases. Currently, the reconstruction of these regions is often based on modified references in which the repetitive regions are masked. However, for many references, such masked genomes are not available or are based on repetitive regions of other genomes. Our idea is to identify repetitive regions in the reference genome de novo. These regions can then be used to reconstruct them separately using short read sequencing data. Afterward, the reconstructed repetitive sequence can be inserted into the reconstructed genome. We present the program detection, characterization, and reconstruction of repetitive regions, which performs these steps automatically. Our results show an increased base pair resolution of the repetitive regions in the reconstruction of Treponema pallidum samples, resulting in fewer unresolved bases.

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

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 from Salmonella enteridis and Streptococcus 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.

Phylogenomic approaches to deciphering the tree of life

Phylogenomics comprises an interdisciplinary field of comparative biology that uses genomic data to produce phylogenetic relationships among organisms (Philippe et al., 2005; Chan & Ragan, 2013). The term “phylogenomics” was initially applied to studies on gene functions (Eisen, 1998) but subsequently has been widely used to infer the tree of life (Philippe et al., 2004,2005; Pennisi, 2008; Zou et al., 2008; Dos Reis et al., 2012; Liu et al., 2015a, 2015b). With the advent and rapid development of next-generation sequencing (NGS), phylogenomics has gained more popularity in the last few years and is now being employed by many members of the evolutionary and systematics community (Lemmon & Lemmon, 2013; Wen et al., 2013, 2015; Jarvis et al., 2014; Weitemier et al., 2014; Wickett et al., 2014; Zeng et al., 2014).

Reflections on Womankind Worldwide's experiences of tackling common challenges in monitoring and evaluating women's rights programming

There are a number of technical, conceptual, and institutional challenges in monitoring and evaluating programme work and advocacy on women's rights. Measuring progress and change is difficult because of the complexity and variety of the work involved in women's rights projects and programmes, and their limited time-frames. In order to capture change, to promote learning within development organisations and the broader sector, and secure donor investment and policymaker commitment to women's rights, it is vital to find creative ways to tackle these challenges. This article presents Womankind Worldwide's and partners' experiences in attempting to tackle some of these challenges by using Outcome Mapping-based approaches, in activities aiming to end violence against women and girls and promote women's participation.

The challenge of increasing vitamin C content in plant foods

The term "vitamin" is used to define a number of organic compounds that have to be obtained from different foods because the organism itself cannot synthesize them in the quantities needed to sustain life. Vitamin C is the common name for L-ascorbic acid. In humans, the principal role of this molecule is to scavenge reactive oxygen species, due to its antioxidant capacity, and to serve as cofactor for many enzymes. A deficiency of L-ascorbic acid is traditionally linked to human diseases such as scurvy. Plant foods are the principal source of L-ascorbic acid for humans. There is a high variability of L-ascorbic acid content in the various plant organs that are used for human consumption. This diversity is related to the specific functions played by L-ascorbic acid in the different plant tissues. The net content of L-ascorbic acid in plants is determined through a balance of the activities of different biosynthetic, recycling, and catabolic pathways. Here we review the importance of L-ascorbic acid for human health, the current knowledge on its metabolism and function in plants, and the efforts that have already been made by genetic modification to improve its content in plant organs used for human food. We provide a current and forward looking perspective of how plant science can contribute to improving the L-ascorbic acid content in crop species using gene transformation, quantitative trait loci and association mapping-based approaches.

State of the art de novo assembly of human genomes from massively parallel sequencing data

Recent studies in human genomes have demonstrated the use of de novo assemblies to identify genetic variations that are difficult for mapping-based approaches. Construction of multiple human genome assemblies is enabled by massively parallel sequencing, but a conventional bioinformatics solution is costly and slow, creating bottle-necks in the process. This review describes two public short-read de novo assembly applications that can handle human genomes, ABySS and SOAPdenovo. It also discusses the technical aspects and future challenges of human genome de novo assembly by short reads.

Guided docking approaches to structure-based design and screening.

With the number of protein-ligand complexes available in the Protein Data Bank constantly growing, structure-based approaches to drug design and screening have become increasingly important. Alongside this explosion of structural information, a number of molecular docking methods have been developed over the last years with the aim of maximally exploiting all available structural and chemical information that can be derived from proteins, from ligands, and from protein-ligand complexes. In this respect, the term 'guided docking' is introduced to refer to docking approaches that incorporate some degree of chemical information to actively guide the orientation of the ligand into the binding site. To reflect the focus on the use of chemical information, a classification scheme for guided docking approaches is proposed. In general terms, guided docking approaches can be divided into indirect and direct approaches. Indirect approaches incorporate chemical information implicitly, having an effect on scoring but not on orienting the ligand during sampling. In contrast, direct approaches incorporate chemical information explicitly, thus actively guiding the orientation of the ligand during sampling. Direct approaches can be further divided into protein-based, mapping-based, and ligand-based approaches to reflect the source used to derive the features capturing the chemical information inside the protein cavity. Within each category, a representative list of docking approaches is discussed. In view of the limitations of current scoring functions, it was generally found that making optimal use of chemical information represents an efficient knowledge-based strategy for improving binding affinity estimations, ligand binding-mode predictions, and virtual screening enrichments obtained from protein-ligand docking.