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7,973 results found. For more information, click each entry to expand.

81: Interaction of quercetin with transcriptional regulator LasR of Pseudomonas aeruginosa: Mechanistic insights of the inhibition of virulence through quorum sensing
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Posted to bioRxiv 27 Dec 2017

Interaction of quercetin with transcriptional regulator LasR of Pseudomonas aeruginosa: Mechanistic insights of the inhibition of virulence through quorum sensing
6,078 downloads bioinformatics

Hovakim Grabski, Lernik Hunanyan, Susanna Tiratsuyan, Hrachik Vardapetyan

Pseudomonas aeruginosa is one of the most dangerous superbugs in the list of bacteria for which new antibiotics are urgently needed, which was published by World Health Organization. P. aeruginosa is an antibiotic-resistant opportunistic human pathogen. It affects patients with AIDS, cystic fibrosis, cancer, burn victims and people with prosthetics and implants. P. aeruginosa also forms biofilms. Biofilms increase resistance to antibiotics and host immune responses. Because of biofilms, current therapies are not effective. It is important to find new antibacterial treatment strategies against P. aeruginosa. Biofilm formation is regulated through a system called quorum sensing. Thus disrupting this system is considered a promising strategy to combat bacterial pathogenicity. It is known that quercetin inhibits Pseudomonas aeruginosa biofilm formation, but the mechanism of action is unknown. In the present study, we tried to analyse the mode of interactions of LasR with quercetin. We used a combination of molecular docking, molecular dynamics (MD) simulations and machine learning techniques for the study of the interaction of the LasR protein of P. aeruginosa with quercetin. We assessed the conformational changes of the interaction and analysed the molecular details of the binding of quercetin with LasR. We show that quercetin has two binding modes. One binding mode is the interaction with ligand binding domain, this interaction is not competitive and it has also been shown experimentally. The second binding mode is the interaction with the bridge, it involves conservative amino acid interactions from LBD, SLR, and DBD and it is also not competitive. Experimental studies show hydroxyl group of ring A is necessary for inhibitory activity, in our model the hydroxyl group interacts with Leu177 during the second binding mode. This could explain the molecular mechanism of how quercetin inhibits LasR protein. This study may offer insights on how quercetin inhibits quorum sensing circuitry by interacting with transcriptional regulator LasR. The capability of having two binding modes may explain why quercetin is effective at inhibiting biofilm formation and virulence gene expression.

82: Strelka2: Fast and accurate variant calling for clinical sequencing applications
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Posted to bioRxiv 23 Sep 2017

Strelka2: Fast and accurate variant calling for clinical sequencing applications
6,059 downloads bioinformatics

Sangtae Kim, Konrad Scheffler, Aaron L Halpern, Mitchell A Bekritsky, Eunho Noh, Morten Källberg, Xiaoyu Chen, Doruk Beyter, Peter Krusche, Christopher T. Saunders

We describe Strelka2 (https://github.com/Illumina/strelka), an open-source small variant calling method for clinical germline and somatic sequencing applications. Strelka2 introduces a novel mixture-model based estimation of indel error parameters from each sample, an efficient tiered haplotype modeling strategy and a normal sample contamination model to improve liquid tumor analysis. For both germline and somatic calling, Strelka2 substantially outperforms current leading tools on both variant calling accuracy and compute cost.

83: Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies
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Posted to bioRxiv 04 Feb 2020

Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies
6,055 downloads bioinformatics

Syed Faraz Ahmed, Ahmed A. Quadeer, Matthew R. McKay

The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally-determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the available SARS-CoV-2 sequences (as of 9 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.

84: Gene expression inference with deep learning
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Posted to bioRxiv 15 Dec 2015

Gene expression inference with deep learning
5,938 downloads bioinformatics

Yifei Chen, Yi Li, Rajiv Narayan, Aravind Subramanian, Xiaohui Xie

Motivation: Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost- effective strategy of profiling only ̃1,000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression, limiting its accuracy since it does not capture complex nonlinear relationship between expression of genes. Results: We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based GEO dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms linear regression with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than linear regression in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2,921 expression profiles. Deep learning still outperforms linear regression with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. Availability: D-GEX is available at https://github.com/uci-cbcl/D-GEX.

85: Real-time search of all bacterial and viral genomic data
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Posted to bioRxiv 15 Dec 2017

Real-time search of all bacterial and viral genomic data
5,838 downloads bioinformatics

Phelim Bradley, Henk C Den Bakker, Eduardo PC Rocha, Gil McVean, Zamin Iqbal

Genome sequencing of pathogens is now ubiquitous in microbiology, and the sequence archives are effectively no longer searchable for arbitrary sequences. Furthermore, the exponential increase of these archives is likely to be further spurred by automated diagnostics. To unlock their use for scientific research and real-time surveillance we have combined knowledge about bacterial genetic variation with ideas used in web-search, to build a DNA search engine for microbial data that can grow incrementally. We indexed the complete global corpus of bacterial and viral whole genome sequence data (447,833 genomes), using four orders of magnitude less storage than previous methods. The method allows future scaling to millions of genomes. This renders the global archive accessible to sequence search, which we demonstrate with three applications: ultra-fast search for resistance genes MCR1-3, analysis of host-range for 2827 plasmids, and quantification of the rise of antibiotic resistance prevalence in the sequence archives.

86: UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing
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Posted to bioRxiv 15 Oct 2016

UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing
5,807 downloads bioinformatics

Robert C. Edgar

Amplicon sequencing of tags such as 16S and ITS ribosomal RNA is a popular method for investigating microbial populations. In such experiments, sequence errors caused by PCR and sequencing are difficult to distinguish from true biological variation. I describe UNOISE2, an updated version of the UNOISE algorithm for denoising (error-correcting) Illumina amplicon reads and show that it has comparable or better accuracy than DADA2.

87: Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models
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Posted to bioRxiv 29 Jan 2019

Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models
5,798 downloads bioinformatics

Chenling Xu, Romain Lopez, Edouard Mehlman, Jeffrey Regier, Michael I. Jordan, Nir Yosef

As single-cell transcriptomics becomes a mainstream technology, the natural next step is to integrate the accumulating data in order to achieve a common ontology of cell types and states. However, owing to various nuisance factors of variation, it is not straightforward how to compare gene expression levels across data sets and how to automatically assign cell type labels in a new data set based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of cohorts of single-cell RNA-seq data sets, while accounting for uncertainty caused by biological and measurement noise. We also introduce single-cell ANnotation using Variational Inference (scANVI), a semi-supervised variant of scVI designed to leverage any available cell state annotations — for instance when only one data set in a cohort is annotated, or when only a few cells in a single data set can be labeled using marker genes. We demonstrate that scVI and scANVI compare favorably to the existing methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings such as a hierarchical structure of cell state labels. We further show that different from existing methods, scVI and scANVI represent the integrated datasets with a single generative model that can be directly used for any probabilistic decision making task, using differential expression as our case study. scVI and scANVI are available as open source software and can be readily used to facilitate cell state annotation and help ensure consistency and reproducibility across studies.

88: MASST: A Web-based Basic Mass Spectrometry Search Tool for Molecules to Search Public Data.
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Posted to bioRxiv 28 Mar 2019

MASST: A Web-based Basic Mass Spectrometry Search Tool for Molecules to Search Public Data.
5,781 downloads bioinformatics

Mingxun Wang, Alan K. Jarmusch, Fernando Vargas, Alexander A. Aksenov, Julia M. Gauglitz, Kelly Weldon, Daniel Petras, Ricardo da Silva, Robby Quinn, Alexey V. Melnik, Justin J.J. van der Hooft, Andrés Mauricio Caraballo Rodríguez, Louis Felix Nothias, Christine M. Aceves, Morgan Panitchpakdi, Elizabeth Brown, Francesca Di Ottavio, Nicole Sikora, Emmanuel O. Elijah, Lara Labarta-Bajo, Emily C. Gentry, Shabnam Shalapour, Kathleen E. Kyle, Sara P. Puckett, Jeramie D. Watrous, Carolina S. Carpenter, Amina Bouslimani, Madeleine Ernst, Austin D. Swafford, Elina I. Zúñiga, Marcy J. Balunas, Jonathan L. Klassen, Rohit Loomba, Rob Knight, Nuno Bandeira, Pieter C. Dorrestein

We introduce a web-enabled small-molecule mass spectrometry (MS) search engine. To date, no tool can query all the public small-molecule tandem MS data in metabolomics repositories, greatly limiting the utility of these resources in clinical, environmental and natural product applications. Therefore, we introduce a Mass Spectrometry Search Tool (MASST) (https://proteosafe-extensions.ucsd.edu/masst/), that enables the discovery of molecular relationships among accessible public metabolomics and natural product tandem mass spectrometry data (MS/MS).

89: Error correction and assembly complexity of single molecule sequencing reads.
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Posted to bioRxiv 18 Jun 2014

Error correction and assembly complexity of single molecule sequencing reads.
5,772 downloads bioinformatics

Hayan Lee, James Gurtowski, Shinjae Yoo, Shoshana Marcus, W. Richard McCombie, Michael Schatz

Third generation single molecule sequencing technology is poised to revolutionize genomics by enabling the sequencing of long, individual molecules of DNA and RNA. These technologies now routinely produce reads exceeding 5,000 basepairs, and can achieve reads as long as 50,000 basepairs. Here we evaluate the limits of single molecule sequencing by assessing the impact of long read sequencing in the assembly of the human genome and 25 other important genomes across the tree of life. From this, we develop a new data-driven model using support vector regression that can accurately predict assembly performance. We also present a novel hybrid error correction algorithm for long PacBio sequencing reads that uses pre-assembled Illumina sequences for the error correction. We apply it several prokaryotic and eukaryotic genomes, and show it can achieve near-perfect assemblies of small genomes (< 100Mbp) and substantially improved assemblies of larger ones. All source code and the assembly model are available open-source.

90: Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images
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Posted to bioRxiv 31 May 2018

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images
5,759 downloads bioinformatics

Juan C. Caicedo, Jonathan Roth, Allen Goodman, Tim Becker, Kyle W Karhohs, Matthieu Broisin, Molnar Csaba, Claire McQuin, Shantanu Singh, FJ Theis, Anne E. Carpenter

Identifying nuclei is often a critical first step in analyzing microscopy images of cells, and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half.

91: Reconstructing cell cycle and disease progression using deep learning
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Posted to bioRxiv 17 Oct 2016

Reconstructing cell cycle and disease progression using deep learning
5,739 downloads bioinformatics

Philipp Eulenberg, Niklas Köhler, Thomas Blasi, Andrew Filby, Anne E. Carpenter, Paul Rees, FJ Theis, F. Alexander Wolf

We show that deep convolutional neural networks combined with non-linear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a 6-fold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer.

92: Single cell RNA-seq denoising using a deep count autoencoder
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Posted to bioRxiv 13 Apr 2018

Single cell RNA-seq denoising using a deep count autoencoder
5,731 downloads bioinformatics

Gökcen Eraslan, Lukas M. Simon, Maria Mircea, Nikola S. Mueller, FJ Theis

Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNAseq data are needed. We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account using a zero-inflated negative binomial noise model, and nonlinear gene-gene or gene-dispersion interactions are captured. Our method scales linearly with the number of cells and can therefore be applied to datasets of millions of cells. We demonstrate that DCA denoising improves a diverse set of typical scRNA-seq data analyses using simulated and real datasets. DCA outperforms existing methods for data imputation in quality and speed, enhancing biological discovery.

93: PICRUSt2: An improved and customizable approach for metagenome inference
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Posted to bioRxiv 15 Jun 2019

PICRUSt2: An improved and customizable approach for metagenome inference
5,706 downloads bioinformatics

Gavin M Douglas, Vincent J. Maffei, Jesse Zaneveld, Svetlana N Yurgel, James R Brown, Christopher M Taylor, Curtis Huttenhower, Morgan G I Langille

One major limitation of microbial community marker gene sequencing is that it does not provide direct information on the functional composition of sampled communities. Here, we present PICRUSt2 (<https://github.com/picrust/picrust2>), which expands the capabilities of the original PICRUSt method[1][1] to predict the functional potential of a community based on marker gene sequencing profiles. This updated method and implementation includes several improvements over the previous algorithm: an expanded database of gene families and reference genomes, a new approach now compatible with any OTU-picking or denoising algorithm, and novel phenotype predictions. Upon evaluation, PICRUSt2 was more accurate than PICRUSt1 and other current approaches overall. PICRUSt2 is also now more flexible and allows the addition of custom reference databases. We highlight these improvements and also important caveats regarding the use of predicted metagenomes, which are related to the inherent challenges of analyzing metagenome data in general. [1]: #ref-1

94: From raw reads to trees: Whole genome SNP phylogenetics across the tree of life
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Posted to bioRxiv 19 Nov 2015

From raw reads to trees: Whole genome SNP phylogenetics across the tree of life
5,678 downloads bioinformatics

Sanaa A. Ahmed, Chien-Chi Lo, Po-E Li, Karen W. Davenport, Patrick S. G. Chain

Next-generation sequencing is increasingly being used to examine closely related organisms. However, while genome-wide single nucleotide polymorphisms (SNPs) provide an excellent resource for phylogenetic reconstruction, to date evolutionary analyses have been performed using different ad hoc methods that are not often widely applicable across different projects. To facilitate the construction of robust phylogenies, we have developed a method for genome-wide identification/characterization of SNPs from sequencing reads and genome assemblies. Our phylogenetic and molecular evolutionary (PhaME) analysis software is unique in its ability to take reads and draft/complete genome(s) as input, derive core genome alignments, identify SNPs, construct phylogenies and perform evolutionary analyses. Several examples using genomes and read datasets for bacterial, eukaryotic and viral linages demonstrate the broad and robust functionality of PhaME. Furthermore, the ability to incorporate raw metagenomic reads from clinical samples with suspected infectious agents shows promise for the rapid phylogenetic characterization of pathogens within complex samples.

95: FusionCatcher - a tool for finding somatic fusion genes in paired-end RNA-sequencing data
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Posted to bioRxiv 19 Nov 2014

FusionCatcher - a tool for finding somatic fusion genes in paired-end RNA-sequencing data
5,613 downloads bioinformatics

Daniel Nicorici, Mihaela Şatalan, Henrik Edgren, Sara Kangaspeska, Astrid Murumägi, Olli Kallioniemi, Sami Virtanen, Olavi Kilkku

FusionCatcher is a software tool for finding somatic fusion genes in paired-end RNA-sequencing data from human or other vertebrates. FusionCatcher achieves competitive detection rates and real-time PCR validation rates in RNA-sequencing data from tumor cells. FusionCatcher is available at http://code.google.com/p/fusioncatcher

96: Accurate detection of complex structural variations using single molecule sequencing
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Posted to bioRxiv 28 Jul 2017

Accurate detection of complex structural variations using single molecule sequencing
5,573 downloads bioinformatics

Fritz J. Sedlazeck, Philipp Rescheneder, Moritz Smolka, Han Fang, Maria Nattestad, Arndt von Haeseler, Michael C. Schatz

Structural variations (SVs) are the largest source of genetic variation, but remain poorly understood because of limited genomics technology. Single molecule long read sequencing from Pacific Biosciences and Oxford Nanopore has the potential to dramatically advance the field, although their high error rates challenge existing methods. Addressing this need, we introduce open-source methods for long read alignment (NGMLR, https://github.com/philres/ngmlr) and SV identification (Sniffles, https://github.com/fritzsedlazeck/Sniffles) that enable unprecedented SV sensitivity and precision, including within repeat-rich regions and of complex nested events that can have significant impact on human disorders. Examining several datasets, including healthy and cancerous human genomes, we discover thousands of novel variants using long reads and categorize systematic errors in short-read approaches. NGMLR and Sniffles are further able to automatically filter false events and operate on low amounts of coverage to address the cost factor that has hindered the application of long reads in clinical and research settings.

97: mixOmics: an R package for 'omics feature selection and multiple data integration
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Posted to bioRxiv 14 Feb 2017

mixOmics: an R package for 'omics feature selection and multiple data integration
5,454 downloads bioinformatics

Kiarash Khosrotehrani, Benoît Gautier, Amrit Singh, Kim-Anh Lê Cao

The advent of high throughput technologies has led to a wealth of publicly available 'omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a 'molecular signature') to explain or predict biological conditions, but mainly for a single type of 'omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a system biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous 'omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple 'omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of 'omics data available from the package.

98: Machine Learning-Based State-Of-The-Art Methods For The Classification Of RNA-Seq Data
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Posted to bioRxiv 26 Mar 2017

Machine Learning-Based State-Of-The-Art Methods For The Classification Of RNA-Seq Data
5,439 downloads bioinformatics

Almas Jabeen, Nadeem Ahmad, Khalid Raza

RNA-Seq measures expression levels of several transcripts simultaneously. The identified reads can be gene, exon, or other region of interest. Various computational tools have been developed for studying pathogen or virus from RNA-Seq data by classifying them according to the attributes in several pre-defined classes, but still computational tools and approaches to analyze complex datasets are still lacking. The development of classification models is highly recommended for disease diagnosis and classification, disease monitoring at molecular level as well as researching for potential disease biomarkers. In this chapter, we are going to discuss various machine learning approaches for RNA-Seq data classification and their implementation. Advancements in bioinformatics, along with developments in machine learning based classification, would provide powerful toolboxes for classifying transcriptome information available through RNA-Seq data.

99: Impact of knowledge accumulation on pathway enrichment analysis
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Posted to bioRxiv 19 Apr 2016

Impact of knowledge accumulation on pathway enrichment analysis
5,427 downloads bioinformatics

Lina Wadi, Mona Meyer, Joel Weiser, Lincoln D Stein, Jüri Reimand

Pathway-based interpretation of gene lists is a staple of genome analysis. It depends on frequently updated gene annotation databases. We analyzed the evolution of gene annotations over the past seven years and found that the vocabulary of pathways and processes has doubled. This strongly impacts practical analysis of genes: 80% of publications we surveyed in 2015 used outdated software that only captured 20% of pathway enrichments apparent in current annotations.

100: Fast and Accurate Genomic Analyses using Genome Graphs
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Posted to bioRxiv 27 Sep 2017

Fast and Accurate Genomic Analyses using Genome Graphs
5,425 downloads bioinformatics

Goran Rakocevic, Vladimir Semenyuk, James Spencer, John Browning, Ivan Johnson, Vladan Arsenijevic, Jelena Nadj, Kaushik Ghose, Maria C. Suciu, Sun-Gou Ji, Gülfem Demir, Lizao Li, Berke Ç. Toptaş, Alexey Dolgoborodov, Björn Pollex, Iosif Spulber, Irina Glotova, Péter Kómár, Andrew Stachyra, Yilong Li, Milos Popovic, Wan-Ping Lee, Morten Källberg, Amit Jain, Deniz Kural

The human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, which impairs read alignment and downstream analysis accuracy. Reference genome structures incorporating known genetic variation have been shown to improve the accuracy of genomic analyses, but have so far remained computationally prohibitive for routine large-scale use. Here we present a graph genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million indels. Our Graph Genome Pipeline requires 6.5 hours to process a 30x coverage WGS sample on a system with 36 CPU cores compared with 11 hours required by the GATK Best Practices pipeline. Using complementary benchmarking experiments based on real and simulated data, we show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, or about 20,000 additional variants being detected per sample, while variant calling specificity is unaffected. Structural variations (SVs) incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is a significant advance towards fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.

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