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1. miniMDS: 3D structural inference from high-resolution Hi-C data

   https://doi.org/10.1093/bioinformatics/btx271  

   Rieber, Lila; Mahony, Shaun (July 2017, Bioinformatics)

   Abstract MotivationRecent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods. ResultsWe have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and then reassembles the partitions using low-resolution MDS. miniMDS is faster, more accurate, and uses less memory than existing methods for inferring the human genome at high resolution (10 kbp). Availability and implementationA Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS. Supplementary informationSupplementary data are available at Bioinformatics online. 

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2. Summarizing the solution space in tumor phylogeny inference by multiple consensus trees

   https://doi.org/10.1093/bioinformatics/btz312  

   Aguse, Nuraini; Qi, Yuanyuan; El-Kebir, Mohammed (July 2019, Bioinformatics)

   Abstract MotivationCancer phylogenies are key to studying tumorigenesis and have clinical implications. Due to the heterogeneous nature of cancer and limitations in current sequencing technology, current cancer phylogeny inference methods identify a large solution space of plausible phylogenies. To facilitate further downstream analyses, methods that accurately summarize such a set T of cancer phylogenies are imperative. However, current summary methods are limited to a single consensus tree or graph and may miss important topological features that are present in different subsets of candidate trees. ResultsWe introduce the Multiple Consensus Tree (MCT) problem to simultaneously cluster T and infer a consensus tree for each cluster. We show that MCT is NP-hard, and present an exact algorithm based on mixed integer linear programming (MILP). In addition, we introduce a heuristic algorithm that efficiently identifies high-quality consensus trees, recovering all optimal solutions identified by the MILP in simulated data at a fraction of the time. We demonstrate the applicability of our methods on both simulated and real data, showing that our approach selects the number of clusters depending on the complexity of the solution space T. Availability and implementationhttps://github.com/elkebir-group/MCT. Supplementary informationSupplementary data are available at Bioinformatics online. 

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3. Microbial resolution of whole genome shotgun and 16S amplicon metagenomic sequencing using publicly available NEON data

   https://doi.org/doi.org/10.1371/journal.pone.0228899  

   Brumfield, Kyle D. (February 2020, PloS one)

   Microorganisms are ubiquitous in the biosphere, playing a crucial role in both biogeochemistry of the planet and human health. However, identifying these microorganisms and defining their function are challenging. Widely used approaches in comparative metagenomics, 16S amplicon sequencing and whole genome shotgun sequencing (WGS), have provided access to DNA sequencing analysis to identify microorganisms and evaluate diversity and abundance in various environments. However, advances in parallel high-throughput DNA sequencing in the past decade have introduced major hurdles, namely standardization of methods, data storage, reproducible interoperability of results, and data sharing. The National Ecological Observatory Network (NEON), established by the National Science Foundation, enables all researchers to address queries on a regional to continental scale around a variety of environmental challenges and provide high-quality, integrated, and standardized data from field sites across the U.S. As the amount of metagenomic data continues to grow, standardized procedures that allow results across projects to be assessed and compared is becoming increasingly important in the field of metagenomics. We demonstrate the feasibility of using publicly available NEON soil metagenomic sequencing datasets in combination with open access Metagenomics Rapid Annotation using the Subsystem Technology (MG-RAST) server to illustrate advantages of WGS compared to 16S amplicon sequencing. Four WGS and four 16S amplicon sequence datasets, from surface soil samples prepared by NEON investigators, were selected for comparison, using standardized protocols collected at the same locations in Colorado between April-July 2014. The dominant bacterial phyla detected across samples agreed between sequencing methodologies. However, WGS yielded greater microbial resolution, increased accuracy, and allowed identification of more genera of bacteria, archaea, viruses, and eukaryota, and putative functional genes that would have gone undetected using 16S amplicon sequencing. NEON open data will be useful for future studies characterizing and quantifying complex ecological processes associated with changing aquatic and terrestrial ecosystems. 

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4. HiFine: integrating Hi-C-based and shotgun-based methods to refine binning of metagenomic contigs

   https://doi.org/10.1093/bioinformatics/btac295  

   Du, Yuxuan; Sun, Fengzhu; Birol, ed., Inanc (April 2022, Bioinformatics)

   Abstract MotivationMetagenomic binning aims to retrieve microbial genomes directly from ecosystems by clustering metagenomic contigs assembled from short reads into draft genomic bins. Traditional shotgun-based binning methods depend on the contigs’ composition and abundance profiles and are impaired by the paucity of enough samples to construct reliable co-abundance profiles. When applied to a single sample, shotgun-based binning methods struggle to distinguish closely related species only using composition information. As an alternative binning approach, Hi-C-based binning employs metagenomic Hi-C technique to measure the proximity contacts between metagenomic fragments. However, spurious inter-species Hi-C contacts inevitably generated by incorrect ligations of DNA fragments between species link the contigs from varying genomes, weakening the purity of final draft genomic bins. Therefore, it is imperative to develop a binning pipeline to overcome the shortcomings of both types of binning methods on a single sample. ResultsWe develop HiFine, a novel binning pipeline to refine the binning results of metagenomic contigs by integrating both Hi-C-based and shotgun-based binning tools. HiFine designs a strategy of fragmentation for the original bin sets derived from the Hi-C-based and shotgun-based binning methods, which considerably increases the purity of initial bins, followed by merging fragmented bins and recruiting unbinned contigs. We demonstrate that HiFine significantly improves the existing binning results of both types of binning methods and achieves better performance in constructing species genomes on publicly available datasets. To the best of our knowledge, HiFine is the first pipeline to integrate different types of tools for the binning of metagenomic contigs. Availability and implementationHiFine is available at https://github.com/dyxstat/HiFine. Supplementary informationSupplementary data are available at Bioinformatics online. 

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5. Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data

   https://doi.org/10.1093/bioinformatics/btac254  

   Edrisi, Mohammadamin; Valecha, Monica_V; Chowdary, Sunkara_B_V; Robledo, Sergio; Ogilvie, Huw_A; Posada, David; Zafar, Hamim; Nakhleh, Luay (June 2022, Bioinformatics)

   Abstract MotivationSingle-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing data, such as SCIΦ and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data. ResultsHere, we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCIΦ in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases. Availability and implementationPhylovar is implemented in Python and is publicly available at https://github.com/NakhlehLab/Phylovar. 

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