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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

SKA: Split Kmer Analysis Toolkit for Bacterial Genomic Epidemiology

Genome sequencing is revolutionising infectious disease epidemiology, providing a huge step forward in sensitivity and specificity over more traditional molecular typing techniques. However, the complexity of genome data often means that its analysis and interpretation requires high-performance compute infrastructure and dedicated bioinformatics support. Furthermore, current methods have limitations that can differ between analyses and are often opaque to the user, and their reliance on multiple external dependencies makes reproducibility difficult. Here I introduce SKA, a toolkit for analysis of genome sequence data from closely-related, small, haploid genomes. SKA uses split kmers to rapidly identify variation between genome sequences, making it possible to analyse hundreds of genomes on a standard home computer. Tests on publicly available simulated and real-life data show that SKA is both faster and more efficient than the gold standard methods used today while retaining similar levels of accuracy for epidemiological purposes. SKA can take raw read data or genome assemblies as input and calculate pairwise distances, create single linkage clusters and align genomes to a reference genome or using a reference-free approach. SKA requires few decisions to be made by the user, which, along with its computational efficiency, allows genome analysis to become accessible to those with only basic bioinformatics training. The limitations of SKA are also far more transparent than for current approaches, and future improvements to mitigate these limitations are possible. Overall, SKA is a powerful addition to the armoury of the genomic epidemiologist. SKA source code is available from Github (https://github.com/simonrharris/SKA).

genomics↗

bioRxiv2013

PyRAD: assembly of de novo RADseq loci for phylogenetic analyses

Restriction-site associated genomic markers are a powerful tool for investigating evolutionary questions at the population level, but are limited in their utility at deeper phylogenetic scales where fewer orthologous loci are typically recovered across disparate taxa. While this limitation stems in part from mutations to restriction recognition sites that disrupt data generation, an alternative source of data loss comes from the failure to identify homology during bioinformatic analyses. Clustering methods that allow for lower similarity thresholds and the inclusion of indel variation will perform better at assembling RADseq loci at the phylogenetic scale.\n\nPyRAD is a pipeline to assemble de novo RADseq loci with the aim of optimizing coverage across phylogenetic data sets. It utilizes a wrapper around an alignment-clustering algorithm which allows for indel variation within and between samples, as well as for incomplete overlap among reads (e.g., paired-end). Here I compare PyRAD with the program Stacks in their performance analyzing a simulated RADseq data set that includes indel variation. Indels disrupt clustering of homologous loci in Stacks but not in PyRAD, such that the latter recovers more shared loci across disparate taxa. I show through re-analysis of an empirical RADseq data set that indels are a common feature of such data, even at shallow phylogenetic scales. PyRAD utilizes parallel processing as well as an optional hierarchical clustering method which allow it to rapidly assemble phylogenetic data sets with hundreds of sampled individuals.\n\nAvailabilitySoftware is written in Python and freely available at http://www.dereneaton.com/software/\n\nSupplementScripts to completely reproduce all simulated and empirical analyses are available in the Supplementary Materials.

Bioinformatics↗

bioRxiv2014

KmerStream: Streaming algorithms for k-mer abundance estimation

Motivation: Several applications in bioinformatics, such as genome assemblers and error corrections methods, rely on counting and keeping track of k-mers (substrings of length k). Histograms of k-mer frequencies can give valuable insight into the underlying distribution and indicate the error rate and genome size sampled in the sequencing experiment.\n\nResults: We present KmerStream, a streaming algorithm for computing statistics for high throughput sequencing data based on the frequency of k-mers. The algorithm runs in time linear in the size of the input and the space requirement are logarithmic in the size of the input. This very low space requirement allows us to deal with much larger datasets than previously presented algorithms. We derive a simple model that allows us to estimate the error rate of the sequencing experiment, as well as the genome size, using only the aggregate statistics reported by KmerStream and validate the accuracy on sequences from a PhiX control.\n\nAs an application we show how KmerStream can be used to compute the error rate of a DNA sequencing experiment. We run KmerStream on a set of 2656 whole genome sequenced individuals and compare the error rate to quality values reported by the sequencing equipment. We discover that while the quality values alone are largely reliable as a predictor of error rate, there is considerable variability in the error rates between sequencing runs, even when accounting for reported quality values.\n\nAvailability: The tool KmerStream is written in C++ and is released under a GPL license. It is freely available at https://github.com/pmelsted/KmerStream\n\nContact: pmelsted@hi.is

Bioinformatics↗

bioRxiv2014

Using 2k + 2 bubble searches to find SNPs in k-mer graphs

This preprint is now available in published form as: Using 2k + 2 bubble searches to find SNPs in k-mer graphs, Reda Younsi; Dan MacLean, Bioinformatics 2014; doi: 10.1093/bioinformatics/btu706\n\nSingle Nucleotide Polymorphism (SNP) discovery is an important preliminary for understanding genetic variation. With current sequencing methods we can sample genomes comprehensively. SNPs are found by aligning sequence reads against longer assembled references. De Bruijn graphs are efficient data structures that can deal with the vast amount of data from modern technologies. Recent work has shown that the topology of these graphs captures enough information to allow the detection and characterisation of genetic variants, offering an alternative to alignment-based methods. Such methods rely on depth-first walks of the graph to identify closing bifurcations. These methods are conservative or generate many false-positive results, particularly when traversing highly inter-connected (complex) regions of the graph or in regions of very high coverage.We devised an algorithm that calls SNPs in converted De Bruijn graphs by enumerating 2k + 2 cycles. We evaluated the accuracy of predicted SNPs by comparison with SNP lists from alignment based methods. We tested accuracy of the SNP calling using sequence data from sixteen ecotypes of Arabidopsis thaliana and found that accuracy was high. We found that SNP calling was even across the genome and genomic feature types. Using sequence based attributes of the graph to train a decision tree allowed us to increase accuracy of SNP calls further.Together these results indicate that our algorithm is capable of finding SNPs accurately in complex sub-graphs and potentially comprehensively from whole genome graphs.The source code for a C++ implementation of our algorithm is available under the GNU Public Licence v3 at:https://github.com/redayounsi/2kplus2

Bioinformatics↗

bioRxiv2014

Data mining of Gene expression profiles of Saccharomyces cerevisiae in response to mild heat stress response

We chose yeast as a model organism to explore how eukaryotic cells respond to heat stress. This study provides details on the way yeast responds to temperature changes and is therefore an empirical reference for basic cell research and industrial fermentation of yeast. We use the Qlucore Omics Explorer (QOE) bioinformatics software to analyze the gene expression profiles of the heat stress from Gene Expression Omnibus (GEO). Genes and their expression are listed in heat maps, and the gene function is analyzed against the biological processes and pathways. We can find that the expression of genes changed over time after heat stress. Gene expression changed rapidly from 0 min to 60 min after heat shock, and gene expression stabilized between 60 min to 360 min. The yeast cells begin to adjust themselves to the high temperatures in terms of the level of gene expression at about 60 min. In all of the involved pathways and biological processes, those related to ribosome and nucleic acid metabolism declined in about 15?30 min and those related to starch and sucrose increased in the same time frame. Temperature can be a simple way to control the biological processes and pathways of cell.

Bioinformatics↗

bioRxiv2014

poRe: an R package for the visualization and analysis of nanopore sequencing data

MotivationThe Oxford Nanopore MinION device represents a unique sequencing technology. As a mobile sequencing device powered by the USB port of a laptop, the MinION has huge potential applications. To enable these applications, the bioinformatics community will need to design and build a suite of tools specifically for MinION data.\n\nResultsHere we present poRe, a package for the statistical software R that enables users to manipulate, organize, summarise and visualize MinION nanopore sequencing data. As a packge for R, poRe has been tested on both Windows and Linux. Crucially, the Windows version allows users to analyse MinION data on the Windows laptop attached to the device\n\nAvailabilityPre-built R packages for Windows and Linux are available under a BSD license at http://sourceforge.net/projects/rpore/\n\nContactmick.watson@roslin.ed.ac.uk

Bioinformatics↗

bioRxiv2014

NxTrim: optimized trimming of Illumina mate pair reads

Motivation: Mate pair protocols add to the utility of paired-end sequencing by boosting the genomic distance spanned by each pair of reads, potentially allowing larger repeats to be bridged and resolved. The Illumina Nextera Mate Pair (NMP) protocol employs a circularisation-based strategy that leaves behind 38bp adapter sequences which must be computationally removed from the data. While "adapter trimming" is a well-studied area of bioinformatics, existing tools do not fully exploit the particular properties of NMP data and discard more data than is necessary. Results: We present NxTrim, a tool that strives to discard as little sequence as possible from NMP reads. The sequence either side of the adapter site is triaged into "virtual libraries" of mate pairs, paired-end reads and single-ended reads. When combined, these data boost coverage and can substantially improve the de novo assembly of bacterial genomes.

Bioinformatics↗

bioRxiv2014

Benchmarking undedicated cloud computing providers for analysis of genomic datasets.

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95%CI: 27.5-78.2) for E.coli and 53.5% (95%CI: 34.4-72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95%CI: 211.5-303.1) and 173.9% (95%CI: 134.6-213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.

Bioinformatics↗

bioRxiv2014

edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test

SummaryNext-generation sequencing platforms for measuring digital expression such as RNA-Seq are displacing traditional microarray-based methods in biological experiments. The detection of differentially expressed genes between groups of biological conditions has led to the development of numerous bioinformatics tools, but so far few, exploit the expanded dynamic range afforded by the new technologies. We present edgeRun, an R package that implements an unconditional exact test that is a more powerful version of the exact test in edgeR. This increase in power is especially pronounced for experiments with as few as 2 replicates per condition, for genes with low total expression and with large biological coefficient of variation. In comparison with a panel of other tools, edgeRun consistently captures functionally similar differentially expressed genes.\n\nAvailabilityThe package is freely available under the MIT license from CRAN (http://cran.r-project.org/web/packages/edgeRun)\n\nContactedimont@mail.harvard.edu

Bioinformatics↗

bioRxiv2014

ProbAlign: a re-alignment method for long sequencing reads

The incorrect alignments are a severe problem in variant calling, and remain as a challenge computational issue in Bioinformatics field. Although there have been some methods utilizing the re-alignment approach to tackle the misalignments, a standalone re-alignment tool for long sequencing reads is lacking. Hence, we present a standalone tool to correct the misalignments, called ProbAlign. It can be integrated into the pipelines of not only variant calling but also other genomic applications. We demonstrate the use of re-alignment in two diverse and important genomics fields: variant calling and viral quasispecies reconstruction. First, variant calling results in the Pacific Biosciences SMRT re-sequencing data of NA12878 show that false positives can be reduced by 43.5%, and true positives can be increased by 24.8% averagely, after re-alignment. Second, results in reconstructing a 5-virus-mix show that the viral population can be completely unraveled, and also the estimation of quasispecies frequencies has been improved, after re-alignment. ProbAlign is freely available in the PyroTools toolkit (https://github.com/homopolymer/PyroTools).

Bioinformatics↗

bioRxiv2014

MyGene.info: gene annotation query as a service

Biomedical knowledge is often represented as annotations of biological entities such as genes, genetic variants, diseases, and drugs. For gene annotations, they are fragmented across data repositories like NCBI Entrez, Ensembl, UniProt, and hundreds (or more) of other specialized databases. While the volume and breadth of annotations is valuable, their fragmentation across many data silos is often frustrating and inefficient. Bioinformaticians everywhere must continuously and repetitively engage in data wrangling in an effort to comprehensively integrate knowledge from all these resources, and these uncoordinated efforts represent an enormous duplication of work. We previously released MyGene.info (http://mygene.info) to enable bioinformatics developers to gain programmatic access to gene annotation data through our high-performance web services. This article focuses on the updates to MyGene.info since our last paper (2013 database issue). With the completely re-factored system, MyGene.info now expands the support from the original nine species to over 14K species, covering >17M genes with >50 gene-specific annotation types. Two simple web service endpoints provides high-performance query access to all these aggregated gene annotations. The infrastructure underlying MyGene.info is highly scalable, which offers both high-performance and high-concurrency, and makes MyGene.info particularly suitable for the use cases of real-time applications and analysis pipelines.

Bioinformatics↗

bioRxiv2014

Identifying highly-penetrant disease causal mutations using next generation sequencing: Guide to whole process

Recent technological advances have created challenges for geneticists and a need to adapt to a wide range of new bioinformatics tools and an expanding wealth of publicly available data (e.g. mutation databases, software). This wide range of methods and a diversity of file formats used in sequence analysis is a significant issue, with a considerable amount of time spent before anyone can even attempt to analyse the genetic basis of human disorders. Another point to consider is although many possess just enough knowledge to analyse their data, they do not make full use of the tools and databases that are available and also do not know how their data was created. The primary aim of this review is to document some of the key approaches and provide an analysis schema to make the analysis process more efficient and reliable in the context of discovering highly penetrant causal mutations/genes. This review will also compare the methods used to identify highly penetrant variants when data is obtained from consanguineous individuals as opposed to non-consanguineous; and when Mendelian disorders are analysed as opposed to common-complex disorders.

Bioinformatics↗

bioRxiv2014

Annotating RNA motifs in sequences and alignments

RNA performs a diverse array of important functions across all cellular life. These functions include important roles in translation, building translational machinery and maturing messenger RNA. More recent discoveries include the miRNAs and bacterial sRNAs that regulate gene expression, the thermosensors, riboswitches and other cis-regulatory elements that help prokaryotes sense their environment and eukaryotic piRNAs that suppress transposition. However, there can be a long period between the initial discovery of a RNA and determining its function. We present a bioinformatic approach to characterise RNA motifs, which are the central building blocks of RNA structure. These motifs can, in some instances, provide researchers with functional hypotheses for uncharacterised RNAs. Moreover, we introduce a new profile-based database of RNA motifs - RMfam - and illustrate its application for investigating the evolution and functional characterisation of RNA.\n\nAll the data and scripts associated with this work is available from: https://github.com/ppgardne/RMfam

Bioinformatics↗

bioRxiv2014

CLASS: Accurate and Efficient Splice Variant Annotation from RNA-seq Reads

Next generation sequencing of cellular RNA is making it possible to characterize genes and alternative splicing in unprecedented detail. However, designing bioinformatics tools to capture splicing variation accurately has proven difficult. Current programs find major isoforms of a gene but miss finer splicing variations, or are sensitive but highly imprecise. We present CLASS, a novel open source tool for accurate genome-guided transcriptome assembly from RNA-seq reads. CLASS employs a splice graph to represent a gene and its splice variants, combined with a linear program to determine an accurate set of exons and efficient splice graph-based transcript selection algorithms. When compared against reference programs, CLASS had the best overall accuracy and could detect up to twice as many splicing events with precision similar to the best reference program. Notably, it was the only tool that produced consistently reliable transcript models for a wide range of applications and sequencing strategies, including very large data sets and ribosomal RNA-depleted samples. Lightweight and multi-threaded, CLASS required <3GB RAM and less than one day to analyze a 350 million read set, and is an excellent choice for transcriptomics studies, from clinical RNA sequencing, to alternative splicing analyses, and to the annotation of new genomes.

Bioinformatics↗

bioRxiv2014

SpeedSeq: Ultra-fast personal genome analysis and interpretation

Comprehensive interpretation of human genome sequencing data is a challenging bioinformatic problem that typically requires weeks of analysis, with extensive hands-on expert involvement. This informatics bottleneck inflates genome sequencing costs, poses a computational burden for large-scale projects, and impedes the adoption of time-critical clinical applications such as personalized cancer profiling and newborn disease diagnosis, where the actionable timeframe can measure in hours or days. We developed SpeedSeq, an open-source genome analysis platform that vastly reduces computing time. SpeedSeq accomplishes read alignment, duplicate removal, variant detection and functional annotation of a 50X human genome in <24 hours, even using one low-cost server. SpeedSeq offers competitive or superior performance to current methods for detecting germline and somatic single nucleotide variants (SNVs), indels, and structural variants (SVs) and includes novel functionality for SV genotyping, SV annotation, fusion gene detection, and rapid identification of actionable mutations. SpeedSeq will help bring timely genome analysis into the clinical realm.\n\nAvailability: SpeedSeq is available at https://github.com/cc2qe/speedseq.

Bioinformatics↗

bioRxiv2015

Merging OpenLifeData with SADI services using Galaxy and Docker

Semantic Web technologies have been widely applied in Life Sciences, for example by data providers like OpenLifeData and Web Services frameworks like SADI. The recent OpenLifeData2SADI project offers access to the OpenLifeData data store through SADI services. This paper shows how to merge data from OpenLifeData with other extant SADI services in the Galaxy bioinformatics analysis platform, making semantic data amenable to complex analyses, as a worked example demonstrates. The whole setting is reproducible through a Docker image that includes a pre-configured Galaxy server with data and workflows that constitute the worked example.

Bioinformatics↗

bioRxiv2015

SWORD - a highly efficient protein database search

MotivationProtein database search is one of the fundamental problems in bioinformatics. For decades, it has been explored and solved using different exact and heuristic approaches. However, exponential growth of data in size in recent years has brought significant challenges in improving already existing algorithms. BLASTP has been the most successful tool for protein database search, but is also becoming a bottleneck in many applications. Due to that, many different approaches have been developed to comple-ment or replace BLASTP. In this paper, we present SWORD, an efficient protein database search implementation that runs 3-4 faster than BLASTP in the sensitive mode and up to 18 faster in the fast and less accurate mode and also provides guaranteed optimal alignments for candidate sequences. SWORD is designed to be used in nearly all database search environments, but is especially suitable for large databases. Its sensitivity exceeds that of BLASTP for majority of input datasets.\n\nAvailabilitySword is freely available for download from https://github.com/rvaser/sword\n\nContactrobert.vaser@fer.hr, mile.sikic@fer.hr

Bioinformatics↗

bioRxiv2015

LINKS: Scaffolding genome assemblies with kilobase-long nanopore reads

MotivationOwing to the complexity of the assembly problem, we do not yet have complete genome sequences. The difficulty in assembling reads into finished genomes is exacerbated by sequence repeats and the inability of short reads to capture sufficient genomic information to resolve those problematic regions. Established and emerging long read technologies show great promise in this regard, but their current associated higher error rates typically require computational base correction and/or additional bioinformatics preprocessing before they could be of value. We present LINKS, the Long Interval Nucleotide K-mer Scaffolder algorithm, a solution that makes use of the information in error-rich long reads, without the need for read alignment or base correction. We show how the contiguity of an ABySS E. coli K-12 genome assembly could be increased over five-fold by the use of beta-released Oxford Nanopore Ltd. (ONT) long reads and how LINKS leverages long-range information in S. cerevisiae W303 ONT reads to yield an assembly with less than half the errors of competing applications. Re-scaffolding the colossal white spruce assembly draft (PG29, 20 Gbp) and how LINKS scales to larger genomes is also presented. We expect LINKS to have broad utility in harnessing the potential of long reads in connecting high-quality sequences of small and large genome assembly drafts.\n\nAvailabilityhttp://www.bcgsc.ca/bioinformatics/software/links\n\nContactrwarren@bcgsc.ca

Bioinformatics↗