The assembly of long reads from Pacific Biosciences and Oxford Nanopore Technologies typically requires resource-intensive error-correction and consensus-generation steps to obtain high-quality ...assemblies. We show that the error-correction step can be omitted and that high-quality consensus sequences can be generated efficiently with a SIMD-accelerated, partial-order alignment-based, stand-alone consensus module called Racon. Based on tests with PacBio and Oxford Nanopore data sets, we show that Racon coupled with miniasm enables consensus genomes with similar or better quality than state-of-the-art methods while being an order of magnitude faster.
Abstract
Motivation
As we move toward an era of precision medicine, the ability to predict patient-specific drug responses in cancer based on molecular information such as gene expression data ...represents both an opportunity and a challenge. In particular, methods are needed that can accommodate the high-dimensionality of data to learn interpretable models capturing drug response mechanisms, as well as providing robust predictions across datasets.
Results
We propose a method based on ideas from 'recommender systems' (CaDRReS) that predicts cancer drug responses for unseen cell-lines/patients based on learning projections for drugs and cell-lines into a latent 'pharmacogenomic' space. Comparisons with other proposed approaches for this problem based on large public datasets (CCLE and GDSC) show that CaDRReS provides consistently good models and robust predictions even across unseen patient-derived cell-line datasets. Analysis of the pharmacogenomic spaces inferred by CaDRReS also suggests that they can be used to understand drug mechanisms, identify cellular subtypes and further characterize drug-pathway associations.
Availability and implementation
Source code and datasets are available at https://github.com/CSB5/CaDRReS.
Supplementary information
Supplementary data are available at Bioinformatics online.
Realizing the democratic promise of nanopore sequencing requires the development of new bioinformatics approaches to deal with its specific error characteristics. Here we present GraphMap, a mapping ...algorithm designed to analyse nanopore sequencing reads, which progressively refines candidate alignments to robustly handle potentially high-error rates and a fast graph traversal to align long reads with speed and high precision (>95%). Evaluation on MinION sequencing data sets against short- and long-read mappers indicates that GraphMap increases mapping sensitivity by 10-80% and maps >95% of bases. GraphMap alignments enabled single-nucleotide variant calling on the human genome with increased sensitivity (15%) over the next best mapper, precise detection of structural variants from length 100 bp to 4 kbp, and species and strain-specific identification of pathogens using MinION reads. GraphMap is available open source under the MIT license at https://github.com/isovic/graphmap.
With the rapid increase in throughput of long-read sequencing technologies, recent studies have explored their potential for taxonomic classification by using alignment-based approaches to reduce the ...impact of higher sequencing error rates. While alignment-based methods are generally slower, k-mer-based taxonomic classifiers can overcome this limitation, potentially at the expense of lower sensitivity for strains and species that are not in the database.
We present MetageNN, a memory-efficient long-read taxonomic classifier that is robust to sequencing errors and missing genomes. MetageNN is a neural network model that uses short k-mer profiles of sequences to reduce the impact of distribution shifts on error-prone long reads. Benchmarking MetageNN against other machine learning approaches for taxonomic classification (GeNet) showed substantial improvements with long-read data (20% improvement in F1 score). By utilizing nanopore sequencing data, MetageNN exhibits improved sensitivity in situations where the reference database is incomplete. It surpasses the alignment-based MetaMaps and MEGAN-LR, as well as the k-mer-based Kraken2 tools, with improvements of 100%, 36%, and 23% respectively at the read-level analysis. Notably, at the community level, MetageNN consistently demonstrated higher sensitivities than the previously mentioned tools. Furthermore, MetageNN requires < 1/4th of the database storage used by Kraken2, MEGAN-LR and MMseqs2 and is > 7× faster than MetaMaps and GeNet and > 2× faster than MEGAN-LR and MMseqs2.
This proof of concept work demonstrates the utility of machine-learning-based methods for taxonomic classification using long reads. MetageNN can be used on sequences not classified by conventional methods and offers an alternative approach for memory-efficient classifiers that can be optimized further.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The study of cell-population heterogeneity in a range of biological systems, from viruses to bacterial isolates to tumor samples, has been transformed by recent advances in sequencing throughput. ...While the high-coverage afforded can be used, in principle, to identify very rare variants in a population, existing ad hoc approaches frequently fail to distinguish true variants from sequencing errors. We report a method (LoFreq) that models sequencing run-specific error rates to accurately call variants occurring in <0.05% of a population. Using simulated and real datasets (viral, bacterial and human), we show that LoFreq has near-perfect specificity, with significantly improved sensitivity compared with existing methods and can efficiently analyze deep Illumina sequencing datasets without resorting to approximations or heuristics. We also present experimental validation for LoFreq on two different platforms (Fluidigm and Sequenom) and its application to call rare somatic variants from exome sequencing datasets for gastric cancer. Source code and executables for LoFreq are freely available at http://sourceforge.net/projects/lofreq/.
Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment ...of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.
Biodiversity is in crisis due to habitat destruction and climate change. The conservation of many noncharismatic species is hampered by the lack of data. Yet, natural history research-a major source ...of information on noncharismatic species-is in decline. We here suggest a remedy for many mammal species, i.e., metagenomic clean-up of fecal samples that are "crowdsourced" during routine field surveys. Based on literature data, we estimate that this approach could yield natural history information for circa 1,000 species within a decade. Metagenomic analysis would simultaneously yield natural history data on diet and gut parasites while enhancing our understanding of host genetics, gut microbiome, and the functional interactions between traditional and new natural history data. We document the power of this approach by carrying out a "metagenomic clean-up" on fecal samples collected during a single night of small mammal trapping in one of Alfred Wallace's favorite collecting sites.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Dengue (DENV) and Zika (ZIKV) viruses are clinically important members of the Flaviviridae family with an 11 kb positive strand RNA genome that folds to enable virus function. Here, we perform ...structure and interaction mapping on four DENV and ZIKV strains inside virions and in infected cells. Comparative analysis of SHAPE reactivities across serotypes nominates potentially functional regions that are highly structured, conserved, and contain low synonymous mutation rates. Interaction mapping by SPLASH identifies many pair-wise interactions, 40% of which form alternative structures, suggesting extensive structural heterogeneity. Analysis of shared interactions between serotypes reveals a conserved macro-organization whereby interactions can be preserved at physical locations beyond sequence identities. We further observe that longer-range interactions are preferentially disrupted inside cells, and show the importance of new interactions in virus fitness. These findings deepen our understanding of Flavivirus genome organization and serve as a resource for designing therapeutics in targeting RNA viruses.
Lessons learnt from the COVID-19 pandemic include increased awareness of the potential for zoonoses and emerging infectious diseases that can adversely affect human health. Although emergent viruses ...are currently in the spotlight, we must not forget the ongoing toll of morbidity and mortality owing to antimicrobial resistance in bacterial pathogens and to vector-borne, foodborne and waterborne diseases. Population growth, planetary change, international travel and medical tourism all contribute to the increasing frequency of infectious disease outbreaks. Surveillance is therefore of crucial importance, but the diversity of microbial pathogens, coupled with resource-intensive methods, compromises our ability to scale-up such efforts. Innovative technologies that are both easy to use and able to simultaneously identify diverse microorganisms (viral, bacterial or fungal) with precision are necessary to enable informed public health decisions. Metagenomics-enabled surveillance methods offer the opportunity to improve detection of both known and yet-to-emerge pathogens.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Fastidious anaerobic bacteria play critical roles in environmental bioremediation of halogenated compounds. However, their characterization and application have been largely impeded by difficulties ...in growing them in pure culture. Thus far, no pure culture has been reported to respire on the notorious polychlorinated biphenyls (PCBs), and functional genes responsible for PCB detoxification remain unknown due to the extremely slow growth of PCB-respiring bacteria. Here we report the successful isolation and characterization of three Dehalococcoides mccartyi strains that respire on commercial PCBs. Using high-throughput metagenomic analysis, combined with traditional culture techniques, tetrachloroethene (PCE) was identified as a feasible alternative to PCBs to isolate PCB-respiring Dehalococcoides from PCB-enriched cultures. With PCE as an alternative electron acceptor, the PCBrespiring Dehalococcoides were boosted to a higher cell density (1.2 × 10⁸ to 1.3 × 10⁸ cells per mL on PCE vs. 5.9 × 10⁻ to 10.4 × 10⁻ cells per mL on PCBs) with a shorter culturing time (30 d on PCE vs. 150 d on PCBs). The transcriptomic profiles illustrated that the distinct PCB dechlorination profile of each strain was predominantly mediated by a single, novel reductive dehalogenase (RDase) catalyzing chlorine removal from both PCBs and PCE. The transcription levels of PCB-RDase genes are 5-60 times higher than the genome-wide average. The cultivation of PCB-respiring Dehalococcoides in pure culture and the identification of PCB-RDase genes deepen our understanding of organohalide respiration of PCBs and shed light on in situ PCB bioremediation.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
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