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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2015

Minimum Information for Reporting Next Generation Sequence Genotyping (MIRING): Guidelines for Reporting HLA and KIR Genotyping via Next Generation Sequencing

The development of next-generation sequencing (NGS) technologies for HLA and KIR genotyping is rapidly advancing knowledge of genetic variation of these highly polymorphic loci. NGS genotyping is poised to replace older methods for clinical use, but standard methods for reporting and exchanging these new, high quality genotype data are needed. The Immunogenomic NGS Consortium, a broad collaboration of histocompatibility and immunogenetics clinicians, researchers, instrument manufacturers and software developers, has developed the Minimum Information for Reporting Immunogenomic NGS Genotyping (MIRING) reporting guidelines. MIRING is a checklist that specifies the content of NGS genotyping results as well as a set of messaging guidelines for reporting the results. A MIRING message includes five categories of structured information - message annotation, reference context, full genotype, consensus sequence and novel polymorphism - and references to three categories of accessory information - NGS platform documentation, read processing documentation and primary data. These eight categories of information ensure the long-term portability and broad application of this NGS data for all current histocompatibility and immunogenetics use cases. In addition, MIRING can be extended to allow the reporting of genotype data generated using pre-NGS technologies. Because genotyping results reported using MIRING are easily updated in accordance with reference and nomenclature databases, MIRING represents a bold departure from previous methods of reporting HLA and KIR genotyping results, which have provided static and less-portable data. More information about MIRING can be found online at miring.immunogenomics.org.

Bioinformatics↗

bioRxiv2015

Flaw or discovery? Calculating exact p-values for genome-wide association studies in inbred populations

MotivationGenome-wide association studies have been conducted in inbred populations where the sample size is small. The ordinary association p-values and multiple testing correction therefore become questionable, as the detected genetic effect may or may not be due to chance, depending on the minor allele frequency distribution across the genome. Instead of permutation testing, marker-specific false positive rate can be analytically calculated in inbred populations without heterozygotes.\n\nResultsSolutions of exact p-values for genome-wide association studies in inbred populations were derived and implemented. An example is presented to illustrate that the marker-specific experiment-wise p-value varies as the genome-wide minor allele frequency distribution changes. A simulation using real Arabidopsis thaliana genome indicates that the use of exact p-values improves detection power and reduces inflation due to population structure. An analysis of a defense-related case-control phenotype using the exact p-values revealed the causal locus, where markers with higher MAFs had smaller p-values than the top variants with lower MAFs in ordinary genome-wide association analysis.\n\nAvailability and ImplementationProject URL: https://r-forge.r-project.org/projects/statomics/. The R package p.exact: https://r-forge.r-project.org/R/?group_id=2030.\n\nContactxia.shen@ki.se

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bioRxiv2015

Flexible expressed region analysis for RNA-seq with derfinder

BackgroundDifferential expression analysis of RNA sequencing (RNA-seq) data typically relies on reconstructing transcripts or counting reads that overlap known gene structures. We previously introduced an intermediate statistical approach called differentially expressed region (DER) finder that seeks to identify contiguous regions of the genome showing differential expression signal at single base resolution without relying on existing annotation or potentially inaccurate transcript assembly.\n\nResultsWe present the derfinder software that improves our annotation-agnostic approach to RNA-seq analysis by: (1) implementing a computationally efficient bump-hunting approach to identify DERs which permits genome-scale analyses in a large number of samples, (2) introducing a flexible statistical modeling framework, including multi-group and time-course analyses and (3) introducing a new set of data visualizations for expressed region analysis. We apply this approach to public RNA-seq data from the Genotype-Tissue Expression (GTEx) project and BrainSpan project to show that derfinder permits the analysis of hundreds of samples at base resolution in R, identifies expression outside of known gene boundaries and can be used to visualize expressed regions at base-resolution. In simulations our base resolution approaches enable discovery in the presence of incomplete annotation and is nearly as powerful as feature-level methods when the annotation is complete.\n\nConclusionsderfinder analysis using expressed region-level and single base-level approaches provides a compromise between full transcript reconstruction and feature-level analysis.\n\nThe package is available from Bioconductor at www.bioconductor.org/packages/derfinder.

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bioRxiv2015

Differential Evolution Approach to Detect Recent Admixture

The genetic structure of human populations is extraordinarily complex and of fundamental importance to studies of anthropology, evolution, and medicine. As increasingly many individuals are of mixed origin, there is an unmet need for tools that can infer multiple origins. Misclassification of such individuals can lead to incorrect and costly misinterpretations of genomic data, primarily in disease studies and drug trials. We present an advanced tool to infer ancestry that can identify the biogeographic origins of highly mixed individuals. reAdmix can incorporate individuals knowledge of ancestors (e.g. having some ancestors from Turkey or a Scottish grandmother). reAdmix is an online tool available at http://chcb.saban-chla.usc.edu/reAdmix/.

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bioRxiv2015

The Use of Distributions in SBML Models

In this technical note we describe modifications to Antimony (Biochemical model specification language) that allows modelers to use the SBML distributions package. In addition the article describes best practice for using distributions, including when they should and should not be used.

Bioinformatics↗

bioRxiv2015

SHAPE directed RNA folding

SummaryChemical mapping experiments allow for nucleotide resolution assessment of RNA structure. We demonstrate that different strategies of integrating probing data with thermodynamics-based RNA secondary structure prediction algorithms can be implemented by means of soft constraints. This amounts to incorporating suitable pseudo-energies into the standard energy model for RNA secondary structures. As a showcase application for this new feature of the viennaRNA Package we compare three distinct, previously published strategies to utilize SHAPE reactivities for structure prediction. The new tool is benchmarked on a set of RNAs with known reference structure.\n\nAvailability and implementationThe capability for SHAPE directed RNA folding is part of the upcoming release of the viennaRNA Package 2.2, for which a preliminary release is already freely available at http://www.tbi.univie.ac.at/RNA.\n\nContactmichael.wolfinger@univie.ac.at\n\nSupplementary informationSupplementary data is attached.

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bioRxiv2015

A complete bacterial genome assembled de novo using only nanopore sequencing data

A method for de novo assembly of data from the Oxford Nanopore MinION instrument is presented which is able to reconstruct the sequence of an entire bacterial chromosome in a single contig. Initially, overlaps between nanopore reads are detected. Reads are then subjected to one or more rounds of error correction by a multiple alignment process employing partial order graphs. After correction, reads are assembled using the Celera assembler. Finally, the assembly is polished using signal-level data from the nanopore employing a novel hidden Markov model. We show that this method is able to assemble nanopore reads from Escherichia coli K-12 MG1655 into a single contig of length 4.6Mb permitting a full reconstruction of gene order. The resulting draft assembly has 98.4% nucleotide identity compared to the finished reference genome. After polishing the assembly with our signal-level HMM, the nucleotide identity is improved to 99.4%. We show that MinION sequencing data can be used to reconstruct genomes without the need for a reference sequence or data from other sequencing platforms.

Bioinformatics↗

bioRxiv2015

The In Silico Genotyper (ISG): an open-source pipeline to rapidly identify and annotate nucleotide variants for comparative genomics applications

The identification and annotation of nucleotide variants, including insertions/deletions and single nucleotide polymorphisms (SNPs), from whole genome sequence data is important for studies of bacterial evolution, comparative genomics, and phylogeography. The in Silico Genotyper (ISG) represents a parallel, tested, open source tool that can perform these functions and scales well to thousands of bacterial genomes. ISG is written in Java and requires MUMmer (Delcher, et al., 2003), BWA (Li and Durbin, 2009), and GATK (McKenna, et al., 2010) for full functionality. The source code and compiled binaries are freely available from https://github.com/TGenNorth/ISGPipeline under a GNU General Public License. Benchmark comparisons demonstrate that ISG is faster and more flexible than comparable tools.

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bioRxiv2015

Quality assessment for different haplotyping methods and GWAS sensitivity to phasing errors

In this report we present a multimarker association tool (Flash) based on a novel algorithm to generate haplotypes from raw genotype data. It belongs to the entropy minimization class of methods [4, 7] and is composed of a two stage deterministic - heuristic part and of a optional stochastic optimization. This algorithm is able to scale up well to handle huge datasets with faster performance than the competing technologies such as BEAGLE[5] and MACH[10] while maintaining a comparable accuracy. A quality assessment of the results is carried out by comparing the switch error. Finally, the haplotypes are used to perform a haplotype-based Genome-wide Association Study (GWAS). The association results are compared with a multimarker and a single SNP association test performed with Plink [12]. Our experiments confirm that the multimarker association test can be more powerful than the single SNP one as stated in the literature. Moreover, Flash and Plink show similar results for the multimarker association test but Flash speeds up the computation time of about an order of magnitude using 5 SNP size haplotypes.

Bioinformatics↗

bioRxiv2015

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk, and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves LD-pruning markers and applying a P-value threshold to association statistics, but this discards information and may reduce predictive accuracy. We introduce a new method, LDpred, which infers the posterior mean causal effect size of each marker using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the pruning/thresholding approach, particularly at large sample sizes. Accordingly, prediction R2 increased from 20.1% to 25.3% in a large schizophrenia data set and from 9.8% to 12.0% in a large multiple sclerosis data set. A similar relative improvement in accuracy was observed for three additional large disease data sets and when predicting in non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Bioinformatics↗

bioRxiv2015

BamHash: a checksum program for verifying the integrity of sequence data

SummaryLarge resequencing projects require a significant amount of storage for raw sequences, as well as alignment files. Since the raw sequences are redundant once the alignment has been generated, it is possible to keep only the alignment files. We present BamHash, a checksum based method to ensure that the read pairs in FASTQ files match exactly the read pairs stored in BAM files, regardless of the ordering of reads. BamHash can be used to verify the integrity of the files stored and discover any discrepancies. Thus, BamHash can be used to determine if it is safe to delete the FASTQ files storing raw sequencing reads after alignment, without the loss of data.\n\nAvailability and ImplementationThe software is implemented in C++, GPL licensed and available at https://github.com/DecodeGenetics/BamHash\n\nContactpmelsted@hi.is

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bioRxiv2015

Similarity Estimation Between DNA Sequences Based on Local Pattern Histograms of Binary Images

Graphical representation of DNA sequences is one of the most popular techniques of alignment-free sequence comparison. In this article, we propose a new method for extracting features of DNA sequences represented by binary images, in which we estimate the similarity between DNA sequences by the frequency histograms of local bitmap patterns on the images. Our method has linear time complexity for the length of DNA sequences, which is practical even for comparison of long sequences. We tested five distance measures to estimate sequence similarities and found that histogram intersection and Manhattan distance are most appropriate for our method among them.

Bioinformatics↗

bioRxiv2015

Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads

BackgroundRNA sequencing (RNA-seq) is a powerful tool for genome-wide expression profiling of biological samples with the advantage of high-throughput and high resolution. There are many existing algorithms nowadays for quantifying expression levels and detecting differential gene expression, but none of them takes the misaligned reads that are mapped to non-exonic regions into account. We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes.\n\nResultsWe implemented our novel XBSeq algorithm and evaluated it by using a set of simulated expression datasets under different conditions, using a combination of negative binomial and Poisson distributions with parameters derived from real RNA-seq data. We compared the performance of our method with other commonly used differential expression analysis algorithms. We also evaluated the changes in true and false positive rates with variations in biological replicates, differential fold changes, and expression levels in non-exonic regions. We also tested the algorithm on a set of real RNA-seq data where the common and different detection results from different algorithms were reported.\n\nConclusionsIn this paper, we proposed a novel XBSeq, a differential expression analysis algorithm for RNA-seq data that takes non-exonic mapped reads into consideration. When background noise is at baseline level, the performance of XBSeq and DESeq are mostly equivalent. However, our method surpasses DESeq and other algorithms with the increase of non-exonic mapped reads. Only in very low read count condition XBSeq had a slightly higher false discovery rate, which may be improved by adjusting the background noise effect in this situation. Taken together, by considering non-exonic mapped reads, XBSeq can provide accurate expression measurement and thus detect differential expressed genes even in noisy conditions.

Bioinformatics↗

bioRxiv2015

Revisiting the Structure/Function Relationships of H/ACA(-like) RNAs: A Unified Model for Euryarchaea and Crenarchaea

A structural and functional classification of H/ACA and H/ACA-like motifs is proposed from the analysis of the H/ACA guide RNAs which have been identified previously in the genomes of Euryarchaea (Pyrococcus) and Crenarchaea (Pyrobaculum). A unified structure/function model is proposed based on the common structural determinants shared by H/ACA and H/ACA-like motifs in both Euryarchaea and Crenarchaea. Using a computational approach, structural and energetic rules for the guide-target RNA-RNA interactions are derived from structural and functional data on the H/ACA RNP particles. H/ACA(-like) motifs found in Pyrococcus are evaluated through the classification and their biological relevance is discussed. Extra-ribosomal targets found in both Pyrococcus and Pyrobaculum are presented as testable gene candidates which might support the hypothesis of a gene regulation mediated by H/ACA(-like) guide RNAs.

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bioRxiv2015

ISMapper: Identifying insertion sequences in bacterial genomes from short read sequence data

BackgroundInsertion sequences (IS) are small transposable elements, commonly found in bacterial genomes. Identifying the location of IS in bacterial genomes can be useful for a variety of purposes including epidemiological tracking and predicting antibiotic resistance. However IS are commonly present in multiple copies in a single genome, which complicates genome assembly and the identification of IS insertion sites. Here we present ISMapper, a mapping-based tool for identification of the site and orientation of IS insertions in bacterial genomes, direct from paired-end short read data.\n\nResultsISMapper was validated using three types of short read data: (i) simulated reads from a variety of species, (ii) Illumina reads from 5 isolates for which finished genome sequences were available for comparison, and (iii) Illumina reads from 7 Acinetobacter baumannii isolates for which predicted IS locations were tested using PCR. A total of 20 genomes, including 13 species and 32 distinct IS, were used for validation. ISMapper correctly identified 96% of known IS insertions in the analysis of simulated reads, and 98% in real Illumina reads. Subsampling of real Illumina reads to lower depths indicated ISMapper was reliable for average genome-wide read depths >20x. All ISAba1 insertions identified by ISMapper in the A. baumannii genomes were confirmed by PCR. In each A. baumannii genome, ISMapper successfully identified an IS insertion upstream of the ampC beta-lactamase that could explain phenotypic resistance to third-generation cephalosporins. The utility of ISMapper was further demonstrated by profiling genome-wide IS6110 insertions in 138 publicly available Mycobacterium tuberculosis genomes, revealing lineage-speific inserction and multi inserction hotspot.\n\nConclusionsISMapper provides a rapid and robust method for identifying IS insertion sites direct from short read data, with a high degree of accuracy demonstrated across a wide range of bacteria.

Bioinformatics↗

bioRxiv2015

Metassembler: Merging and optimizing de novo genome assemblies

Genome assembly projects typically run multiple algorithms in an attempt to find the single best assembly, although those assemblies often have complementary, if untapped, strengths and weaknesses. We present our metassembler algorithm that merges multiple assemblies of a genome into a single superior sequence. We apply it to the four genomes from the Assemblathon competitions and show it consistently and substantially improves the contiguity and quality of each assembly. We also develop guidelines for metassembly by systematically evaluating 120 permutations of merging the top 5 assemblies of the first Assemblathon competition. The software is open-source at http://metassembler.sourceforge.net.

Bioinformatics↗

bioRxiv2015

Latent epistatic interaction model identifies loci associated with human working memory

BackgroundEpistatic interactions among genomic loci are expected to explain a large fraction of the heritability of complex diseases and phenotypic traits of living organisms.\n\nAlthough epistasis detection methods are continually being developed, the current state of the art is exhaustive search methods, which become infeasible when the number of analyzed loci is large.\n\nResultsWe develop a novel latent interaction-based selection method for polymorphic loci as the first stage of a two-stage epistasis detection approach. Given a continuous phenotype and a single-nucleotide polymorphism (SNP), we rank the SNPs according to their interaction potential. When tested on simulated datasets and compared to standard marginal association and exhaustive search methods, our procedure significantly outperforms main-effect heuristics, especially in the presence of linkage disequilibrium (LD), which is explicitly accounted for in our model. Applied to real human genotype data, we prioritized several SNP pairs as candidates for epistatic interactions that influence human working memory performance, some of which are known to be connected to this phenotype.\n\nConclusionsThe proposed method improves two-stage epistasis detection. Its linear runtime and increased statistical power contribute to reducing the computational complexity and to addressing some of the statistical challenges associated with the genome-wide search for epistatic loci.

Bioinformatics↗

bioRxiv2015

regionReport: Interactive reports for region-based analyses

regionReport is a R package for generating detailed interactive reports from regions of the genome. The report includes quality-control checks, an overview of the results, an interactive table of the genomic regions, and reproducibility information. regionReport can easily be expanded with report templates for other specialized analyses. In particular, regionReport has an extensive report template for exploring derfinder results from annotation-agnostic RNA-seq differential expression analyses.\n\nAvailabilityregionReport is freely available via Bioconductor at bioconductor.org/packages/release/bioc/html/regionReport.html.

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