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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Isaac, the Illumina variant calling workflow

As the pace of implementing personalized medicine concepts increases, high-throughput variant calling on hundreds of individual genomes per day is a reality that will likely be faced by sequencing facilities across the country in the near future. While the scientific best practices for human variant calling workflows have been well defined, they also pose serious computational challenges at this high scale. Therefore, efforts in both academia and the private sector have focused on developing alternative workflows that may substantially reduce the computational cost per individual genome. Isaac is an \"ultra-fast\" variant calling workflow, designed by Illumina, Inc, and is claimed to be six times faster than BWA-GATK, with comparable sensitivity and specificity. This report is an independent review of Isaac, mainly focused on the accuracy of variant calls. We note that Isaac is indeed quite fast, and provide some benchmarks on a few hardware architectures. The overall conclusion from our analysis is that the Isaac workflow has undergone substantial improvement from version 01.14.11.27 to Isaac_2.0. The call accuracy is especially high on NA12878, however exomes tend to have a high fraction of false positive calls. We did not manage to reproduce the 99% sensitivity and specificity reported in the Illumina whitepaper, however that might be improved with further tweaking of the options. This report includes the information about some of the command-line parameters and documentation.

Bioinformatics↗
bioRxiv2015

MicrobiomeGWAS: a tool for identifying host genetic variants associated with microbiome composition

The microbiome is the collection of all microbial genes and can be investigated by sequencing highly variable regions of 16S ribosomal RNA (rRNA) genes. Evidence suggests that environmental factors and host genetics may interact to impact human microbiome composition. Identifying host genetic variants associated with human microbiome composition not only provides clues for characterizing microbiome variation but also helps to elucidate biological mechanisms of genetic associations, prioritize genetic variants, and improve genetic risk prediction. Since a microbiota functions as a community, it is best characterized by beta diversity, that is, a pairwise distance matrix. We develop a statistical framework and a computationally efficient software package, microbiomeGWAS, for identifying host genetic variants associated with microbiome beta diversity with or without interacting with an environmental factor. We show that score statistics have positive skewness and kurtosis due to the dependent nature of the pairwise data, which makes P-value approximations based on asymptotic distributions unacceptably liberal. By correcting for skewness and kurtosis, we develop accurate P-value approximations, whose accuracy was verified by extensive simulations. We exemplify our methods by analyzing a set of 147 genotyped subjects with 16S rRNA microbiome profiles from non-malignant lung tissues. Correcting for skewness and kurtosis eliminated the dramatic deviation in the quantile-quantile plots. We provided preliminary evidence that six established lung cancer risk SNPs were collectively associated with microbiome composition for both unweighted (P=0.0032) and weighted (P=0.011) UniFrac distance matrices. In summary, our methods will facilitate analyzing large-scale genome-wide association studies of the human microbiome.

Bioinformatics↗
bioRxiv2015

CRISPResso: sequencing analysis toolbox for CRISPR genome editing

To the Editor To the Editor References Recent progress in genome editing technologies, in particular the CRISPR-Cas9 nuclease system, has provided new opportunities to investigate the biological functions of genomic sequences by targeted mutagenesis [1-4]. Briefly, Cas9 may be directed by a chimeric single guide RNA (sgRNA) to a target genomic sequence upstream of a protospacer adjacent motif (PAM) for cleavage. Double strand breaks (DSBs) resulting from site-specific Cas9 cleavage can be resolved by endogenous DNA repair pathways such as non-homologous end joining (NHEJ) or homology-directed repair (HDR). These repair mechanisms result in a spectrum of diverse outcomes including insertions, deletions, nucleotide substitutions, and, in the case of HDR, recombination of extrachromosomal donor sequences [1-3, 5 ...

Bioinformatics↗
bioRxiv2015

Kaiju: Fast and sensitive taxonomic classification for metagenomics

The constantly decreasing cost and increasing out-put of current sequencing technologies enable large scale metagenomic studies of microbial communities from diverse habitats. Therefore, fast and accurate methods for taxonomic classification are needed, which can operate on increasingly larger datasets and reference databases. Recently, several fast metagenomic classifiers have been developed, which are based on comparison of genomic k-mers. However, nucleotide comparison using a fixed k-mer length often lacks the sensitivity to overcome the evolutionary distance between sampled species and genomes in the reference database. Here, we present the novel metagenome classifier Kaiju for fast assignment of reads to taxa. Kaiju finds maximum exact matches on the protein-level using the Borrows-Wheeler transform, and can optionally allow amino acid substitutions in the search using a greedy heuristic. We show in a genome exclusion study that Kaiju can classify more reads with higher sensitivity and similar precision compared to fast k-mer based classifiers, especially in genera that are underrepresented in reference databases. We also demonstrate that Kaiju classifies more than twice as many reads in ten real metagenomes compared to programs based on genomic k-mers. Kaiju can process up to millions of reads per minute, and its memory footprint is below 6 GB of RAM, allowing the analysis on a standard PC. The program is available under the GPL3 license at: http://bioinformatics-centre.github.io/kaiju

Bioinformatics↗
bioRxiv2016

Arkas: Rapid and Reproducible RNAseq Analysis for End Users

The recently introduced Kallisto[1] pseudoaligner has radically simplified the quantification of transcripts in RNA-sequencing experiments. However, as with all computational advances, reproducibility across experiments requires attention to detail. The elegant approach of Kallisto reduces dependencies, but we noted differences in quantification between versions of Kallisto, and both upstream preparation and downstream interpretation benefit from an environment that enforces a requirement for equivalent processing when comparing groups of samples. Therefore, we created the Arkas[3] and TxDbLite[4] R packages to meet these needs and to ease cloud-scale deployment of the above. TxDbLite extracts structured information directly from source FASTA files with per-contig metadata, while Arkas enforces versioning of the derived indices and annotations, to ensure tight coupling of inputs and outputs while minimizing external dependencies. The two packages are combined in Illumina's BaseSpace cloud computing environment to offer a massively parallel and distributed quantification step for power users, loosely coupled to biologically informative downstream analyses via gene set analysis (with special focus on Reactome annotations for ENSEMBL transcriptomes). Previous work (e.g. Soneson et al., 2016[34]) has revealed that filtering transcriptomes to exclude lowly-expressed isoforms can improve statistical power, while more-complete transcriptome assemblies improve sensitivity in detecting differential transcript usage. Based on earlier work by Bourgon et al., 2010[11], we included this type of filtering for both gene- and transcript-level analyses within Arkas. For reproducible and versioned downstream analysis of results, we focused our efforts on ENSEMBL and Reac-tome[2] integration within the qusage[19] framework, adapted to take advantage of the parallel and distributed environment in Illuminas BaseSpace cloud platform. We show that quantification and interpretation of repetitive sequence element transcription is eased in both basic and clinical studies by just-in-time annotation and visualization. The option to retain pseudoBAM output for structural variant detection and annotation, while not insignificant in its demand for computation and storage, nonetheless provides a middle ground between de novo transcriptome assembly and routine quantification, while consuming a fraction of the resources used by popular fusion detection pipelines and providing options to quantify gene fusions with known breakpoints without reassembly. Finally, we describe common use cases where investigators are better served by cloud-based computing platforms such as BaseSpace due to inherent efficiencies of scale and enlightened common self-interest. Our experiences suggest a common reference point for methods development, evaluation, and experimental interpretation.

Bioinformatics↗
bioRxiv2015

Rapid Genotype Refinement for Whole-Genome Sequencing Data using Multi-Variate Normal Distributions

Whole-genome low-coverage sequencing has been combined with linkage-disequilibrium (LD) based genotype refinement to accurately and cost-effectively infer genotypes in large cohorts of individuals. Most genotype refinement methods are based on hidden Markov models, which are accurate but computationally expensive. We introduce an algorithm that models LD using a simple multivariate Gaussian distribution. The key feature of our algorithm is its speed, it is hundreds of times faster than other methods on the same data set and its scaling behaviour is linear in the number of samples. We demonstrate the performance of the method on both low-coverage and high-coverage samples.\n\nAvailability: The source code is available at https://github.com/sequencing/marvin\n\nContact: rarthur@illumina.com

Bioinformatics↗
bioRxiv2015

Towards extracting supporting information about predicted protein-protein interactions

One of the goals of relation extraction is to identify protein-protein interactions (PPIs) in biomedical literature. Current systems are capturing binary relations and also the direction and type of an interaction. Besides assisting in the curation PPIs into databases, there has been little real-world application of these algorithms. We describe UPSITE, a text mining tool for extracting evidence in support of a hypothesized interaction. Given a predicted PPI, UPSITE uses a binary relation detector to check whether a PPI is found in abstracts in PubMed. If it is not found, UPSITE retrieves documents relevant to each of the two proteins separately, and extracts contextual information about biological events surrounding each protein, and calculates semantic similarity of the two proteins to provide evidential support for the predicted PPI. In evaluations, relation extraction achieved an Fscore of 0.88 on the HPRD50 corpus, and semantic similarity measured with angular distance was found to be statistically significant. With the development of PPI prediction algorithms, the burden of interpreting the validity and relevance of novel PPIs is on biologists. We suggest that presenting annotations of the two proteins in a PPI side-by-side and a score that quantifies their similarity lessens this burden to some extent.

Bioinformatics↗
bioRxiv2015

DataJoint: managing big scientific data using MATLAB or Python

The rise of big data in modern research poses serious challenges for data management: Large and intricate datasets from diverse instrumentation must be precisely aligned, annotated, and processed in a variety of ways to extract new insights. While high levels of data integrity are expected, research teams have diverse backgrounds, are geographically dispersed, and rarely possess a primary interest in data science. Here we describe DataJoint, an open-source toolbox designed for manipulating and processing scientific data under the relational data model. Designed for scientists who need a flexible and expressive database language with few basic concepts and operations, DataJoint facilitates multiuser access, efficient queries, and distributed computing. With implementations in both MATLAB and Python, DataJoint is not limited to particular file formats, acquisition systems, or data modalities and can be quickly adapted to new experimental designs. DataJoint and related resources are available at http://datajoint.github.com.

Bioinformatics↗
bioRxiv2015

Learning structure in gene expression data using deep architectures, with an application to gene clustering

Genes play a central role in all biological processes. DNA microarray technology has made it possible to study the expression behavior of thousands of genes in one go. Often, gene expression data is used to generate features for supervised and unsupervised learning tasks. At the same time, advances in the field of deep learning have made available a plethora of architectures. In this paper, we use deep architectures pre-trained in an unsupervised manner using denoising autoencoders as a preprocessing step for a popular unsupervised learning task. Denoising autoencoders (DA) can be used to learn a compact representation of input, and have been used to generate features for further supervised learning tasks. We propose that our deep architectures can be treated as empirical versions of Deep Belief Networks (DBNs). We use our deep architectures to regenerate gene expression time series data for two different data sets. We test our hypothesis on two popular datasets for the unsupervised learning task of clustering and find promising improvements in performance.

Bioinformatics↗
bioRxiv2015

Wikidata: A platform for data integration and dissemination for the life sciences and beyond

Wikidata is an open, Semantic Web-compatible database that anyone can edit. This data commons provides structured data for Wikipedia articles and other applications. Every article on Wikipedia has a hyperlink to an editable item in this database. This unique connection to the worlds largest community of volunteer knowledge editors could help make Wikidata a key hub within the greater Semantic Web. The life sciences, as ever, faces crucial challenges in disseminating and integrating knowledge. Our group is addressing these issues by populating Wikidata with the seeds of a foundational semantic network linking genes, drugs and diseases. Using this content, we are enhancing Wikipedia articles to both increase their quality and recruit human editors to expand and improve the underlying data. We encourage the community to join us as we collaboratively create what can become the most used and most central semantic data resource for the life sciences and beyond.

Bioinformatics↗
bioRxiv2015

Mass graphs and their applications in top-down proteomics

Although proteomics has made rapid progress in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a \"bird view\" of intact proteoforms. The combinatorial explosion of possible proteoforms, which may result in billions of possible proteoforms for one protein, makes proteoform identification a challenging computational problem. Here we propose a new data structure, called the mass graph, for efficiently representing proteoforms. In addition, we design mass graph alignment algorithms for proteoform identification by top-down mass spectrometry. Experiments on a histone H4 mass spectrometry data set showed that the proposed methods outperformed MS-Align-E in identifying complex proteoforms.

Bioinformatics↗
bioRxiv2015

Testing Rare-Variant Association without Calling Genotypes Allows for Systematic Differences in Sequencing between Cases and Controls

Next-generation sequencing of DNA provides an unprecedented opportunity to discover rare genetic variants associated with complex diseases and traits. However, when testing the association between rare variants and traits of interest, the current practice of first calling underlying genotypes and then treating the called values as known is prone to false positive findings, especially when genotyping errors are systematically different between cases and controls. This happens whenever cases and controls are sequenced at different depths or on different platforms. In this article, we provide a likelihood-based approach to testing rare variant associations that directly models sequencing reads without calling genotypes. We consider the (weighted) burden test statistic, which is the (weighted) sum of the score statistic for assessing effects of individual variants on the trait of interest. Because variant locations are unknown, we develop a simple, computationally efficient screening algorithm to estimate the loci that are variants. Because our burden statistic may not have mean zero after screening, we develop a novel bootstrap procedure for assessing the significance of the burden statistic. We demonstrate through extensive simulation studies that the proposed tests are robust to a wide range of differential sequencing qualities between cases and controls, and are at least as powerful as the standard genotype calling approach when the latter controls type I error. An application to the UK10K data reveals novel rare variants in gene BTBD18 associated with childhood onset obesity. The relevant software is freely available.

Bioinformatics↗
bioRxiv2015

Wikidata as a semantic framework for the Gene Wiki initiative

Open biological data is distributed over many resources making it challenging to integrate, to update and to disseminate quickly. Wikidata is a growing, open community database which can serve this purpose and also provides tight integration with Wikipedia.\n\nIn order to improve the state of biological data, facilitate data management and dissemination, we imported all human and mouse genes, and all human and mouse proteins into Wikidata. In total, 59,530 human genes and 73,130 mouse genes have been imported from NCBI and 27,662 human proteins and 16,728 mouse proteins have been imported from the Swissprot subset of UniProt. As Wikidata is open and can be edited by anybody, our corpus of imported data serves as the starting point for integration of further data by scientists, the Wikidata community and citizen scientists alike. The first use case for this data is to populate Wikipedia Gene Wiki infoboxes directly from Wikidata with the data integrated above. This enables immediate updates of the Gene Wiki infoboxes as soon as the data in Wikidata is modified. Although Gene Wiki pages are currently only on the English language version of Wikipedia, the multilingual nature of Wikidata allows for a usage of the data we imported in all 280 different language Wikipedias. Apart from the Gene Wiki infobox use case, a powerful SPARQL endpoint and up to date exporting functionality (e.g. JSON, XML) enable very convenient further use of the data by scientists.\n\nIn summary, we created a fully open and extensible data resource for human and mouse molecular biology and biochemistry data. This resource enriches all the Wikipedias with structured information and serves as a new linking hub for the biological semantic web.

Bioinformatics↗
bioRxiv2015

An evaluation of methods correcting for cell type heterogeneity in DNA methylation studies

Background: Many different methods exist to adjust for variability in cell-type mixture proportions when analyzing DNA methylation studies. Here we present the result of an extensive simulation study, built on cell-separated DNA methylation profiles from Illumina Infinium 450K methylation data, to compare the performance of 8 methods including the most commonly-used approaches. Results: We designed a rich multi-layered simulation containing a set of probes with true associations with either binary or continuous phenotypes, confounding by cell type, variability in means and standard deviations for population parameter, additional variability at the level of an individual cell-type-specific sample, and variability in the mixture proportions across samples. Performance varied quite substantially across methods and simulations. In particular, the false discovery rates (FDR) were sometimes unrealistically high, indicating limited ability to discriminate the true signals from those appearing significant through confounding. Methods that filtered probes had consequently poor power. QQ-plots of p-values across all tested probes showed that adjustments did not always improve the distribution. The same methods were used to examine associations between smoking and methylation data from a case-control study of colorectal cancer. Conclusions: We recommend surrogate variable analysis for cell-type mixture adjustment since performance was stable under all our simulated scenarios.

Bioinformatics↗
bioRxiv2015

XMRF: An R package to Fit Markov Networks to High-Throughput Genetics Data

MotivationTechnological advances in medicine have led to a rapid proliferation of high-throughput \"omics\" data. Tools to mine this data and discover disrupted disease networks are needed as they hold the key to understanding complicated interactions between genes, mutations and aberrations, and epi-genetic markers.\n\nResultsWe developed an R software package, XMRF, that can be used to fit Markov Networks to various types of high-throughput genomics data. Encoding the models and estimation techniques of the recently proposed exponential family Markov Random Fields (Yang et al., 2012), our software can be used to learn genetic networks from RNA-sequencing data (counts via Poisson graphical models), mutation and copy number variation data (categorical via Ising models), and methylation data (continuous via Gaussian graphical models).\n\nAvailabilityXMRF is available from the CRAN Project and Github at: https://github.com/zhandong/XMRF

Bioinformatics↗
bioRxiv2015

Neptune: A Tool for Rapid Microbial Genomic Signature Discovery

The ready availability of vast amounts of genomic sequence data has created the need to rethink comparative genomics algorithms using \"big data\" approaches. Neptune is an efficient system for rapidly locating differentially abundant genomic content in bacterial populations using an exact k-mer matching strategy, while accommodating k-mer mismatches. Neptunes loci discovery process identifies sequences that are sufficiently common to a group of target sequences and sufficiently absent from non-targets using probabilistic models. Neptune uses parallel computing to efficiently identify and extract these loci from draft genome assemblies without requiring multiple sequence alignments or other computationally expensive comparative sequence analyses. Tests on simulated and real data sets showed that Neptune rapidly identifies regions that are both sensitive and specific. We demonstrate that this system can identify trait-specific loci from different bacterial lineages. Neptune is broadly applicable for comparative bacterial analyses, yet will particularly benefit pathogenomic applications, owing to efficient and sensitive discovery of differentially abundant genomic loci.

Bioinformatics↗
bioRxiv2015

From raw reads to trees: Whole genome SNP phylogenetics across the tree of life

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.

Bioinformatics↗