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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2014

Polyester: simulating RNA-seq datasets with differential transcript expression

MotivationStatistical methods development for differential expression analysis of RNA sequencing (RNA-seq) requires software tools to assess accuracy and error rate control. Since true differential expression status is often unknown in experimental datasets, artificially-constructed datasets must be utilized, either by generating costly spike-in experiments or by simulating RNA-seq data.\n\nResultsPolyester is an R package designed to simulate RNA-seq data, beginning with an experimental design and ending with collections of RNA-seq reads. Its main advantage is the ability to simulate reads indicating isoform-level differential expression across biological replicates for a variety of experimental designs. Data generated by Polyester is a reasonable approximation to real RNA-seq data and standard differential expression workflows can recover differential expression set in the simulation by the user.\n\nAvailability and ImplementationPolyester is freely available from Bioconductor (http://bioconductor.org/).\n\nContactjtleek@gmail.com\n\nSupplementary InformationSupplementary figures are available online.

Bioinformatics↗

bioRxiv2014

Reducing INDEL calling errors in whole-genome and exome sequencing data

BackgroundINDELs, especially those disrupting protein-coding regions of the genome, have been strongly associated with human diseases. However, there are still many errors with INDEL variant calling, driven by library preparation, sequencing biases, and algorithm artifacts.\n\nMethodsWe characterized whole genome sequencing (WGS), whole exome sequencing (WES), and PCR-free sequencing data from the same samples to investigate the sources of INDEL errors. We also developed a classification scheme based on the coverage and composition to rank high and low quality INDEL calls. We performed a large-scale validation experiment on 600 loci, and find high-quality INDELs to have a substantially lower error rate than low quality INDELs (7% vs. 51%).\n\nResultsSimulation and experimental data show that assembly based callers are significantly more sensitive and robust for detecting large INDELs (>5 bp) than alignment based callers, consistent with published data. The concordance of INDEL detection between WGS and WES is low (52%), and WGS data uniquely identifies 10.8-fold more high-quality INDELs. The validation rate for WGS-specific INDELs is also much higher than that for WES-specific INDELs (85% vs. 54%), and WES misses many large INDELs. In addition, the concordance for INDEL detection between standard WGS and PCR-free sequencing is 71%, and standard WGS data uniquely identifies 6.3-fold more low-quality INDELs. Furthermore, accurate detection with Scalpel of heterozygous INDELs requires 1.2-fold higher coverage than that for homozygous INDELs. Lastly, homopolymer A/T INDELs are a major source of low-quality INDEL calls, and they are highly enriched in the WES data.\n\nConclusionsOverall, we show that accuracy of INDEL detection with WGS is much greater than WES even in the targeted region. We calculated that 60X WGS depth of coverage from the HiSeq platform is needed to recover 95% of INDELs detected by Scalpel. While this is higher than current sequencing practice, the deeper coverage may save total project costs because of the greater accuracy and sensitivity. Finally, we investigate sources of INDEL errors (e.g. capture deficiency, PCR amplification, homopolymers) with various data that will serve as a guideline to effectively reduce INDEL errors in genome sequencing.

Bioinformatics↗

bioRxiv2014

Evaluation of de novo transcriptome assemblies from RNA-Seq data

De novo RNA-Seq assembly facilitates the study of transcriptomes for species without sequenced genomes, but it is challenging to select the most accurate assembly in this context. To address this challenge, we developed a model-based score, RSEM-EVAL, for evaluating assemblies when the ground truth is unknown. Our experiments show that RSEM-EVAL correctly reflects assembly accuracy, as measured by REF-EVAL, a refined set of ground-truth-based scores that we also developed. With the guidance of RSEM-EVAL, we assembled the transcriptome of the regenerating axolotl limb; this assembly compares favorably to a previous assembly.

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bioRxiv2014

Finite-state discrete-time Markov chain models of gene regulatory networks

In this study Markov chain models of gene regulatory networks (GRN) are developed. These models gives the ability to apply the well known theory and tools of Markov chains to GRN analysis. We introduce a new kind of the finite graph of the interactions called the combinatorial net that formally represent a GRN and the transition graphs constructed from interaction graphs. System dynamics are defined as a random walk on the transition graph that is some Markovian chain. A novel concurrent updating scheme (evolution rule) is developed to determine transitions in a transition graph. Our scheme is based on the firing of a random set of non-steady state vertices of a combinatorial net. We demonstrate that this novel scheme gives an advance in the modeling of the asynchronicity. Also we proof the theorem that the combinatorial nets with this updating scheme can asynchronously compute a maximal independent sets of graphs. As proof of concept, we present here a number of simple combinatorial models: a discrete model of auto-repression, a bi-stable switch, the Elowitz repressilator, a self-activation and show that this models exhibit well known properties.

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bioRxiv2014

Error correction and assembly complexity of single molecule sequencing reads.

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.

Bioinformatics↗

bioRxiv2014

Beyond library size: a field guide to NGS normalization

BackgroundNext generation sequencing (NGS) is a widely used technology in both basic research and clinical settings and it will continue to have a major impact on biomedical sciences. However, the use of incorrect normalization methods can lead to systematic biases and spurious results, making the selection of an appropriate normalization strategy a crucial and often overlooked part of NGS analysis. ResultsWe present a basic introduction to the currently available normalization methods for differential expression and ChIP-seq applications, along with best use recommendations for different experimental techniques and datasets. We demonstrate that the choice of normalization technique can have a significant impact on the number of genes called as differentially expressed in an RNA-seq experiment or peaks called in a ChIP-seq experiment. ConclusionsThe choice of the most adequate normalization method depends on both the distribution of signal in the dataset and the intended downstream applications. Depending on the design and purpose of the study, appropriate bias correction should also be considered.

Bioinformatics↗

bioRxiv2014

Compression of short-read sequences using path encoding

Storing, transmitting, and archiving the amount of data produced by next generation sequencing is becoming a significant computational burden. For example, large-scale RNA-seq meta-analyses may now routinely process tens of terabytes of sequence. We present here an approach to biological sequence compression that reduces the difficulty associated with managing the data produced by large-scale transcriptome sequencing. Our approach offers a new direction by sitting between pure reference-based compression and reference-free compression and combines much of the benefit of reference-based approaches with the flexibility of de novo encoding. Our method, called path encoding, draws a connection between storing paths in de Bruijn graphs --- a common task in genome assembly --- and context-dependent arithmetic coding. Supporting this method is a system, called a bit tree, to compactly store sets of kmers that is of independent interest. Using these techniques, we are able to encode RNA-seq reads using 3% -- 11% of the space of the sequence in raw FASTA files, which is on average more than 34% smaller than recent competing approaches. We also show that even if the reference is very poorly matched to the reads that are being encoded, good compression can still be achieved.

Bioinformatics↗

bioRxiv2014

Accurate prediction of transmembrane β-barrel proteins from sequences

Transmembrane {beta}-barrels are known to play major roles in substrate transport and protein biogenesis in gram-negative bacteria, chloroplasts and mitochondria. However, the exact number of transmembrane {beta}-barrel families is unknown and experimental structure determination is challenging. In theory, if one knows the number of strands in the {beta}-barrel, then the 3D structure of the barrel could be trivial, but current topology predictions do not predict accurate structures and are unable to give information beyond the {beta}-strands in the barrel. Recent work has shown successful prediction of globular and alpha-helical membrane proteins from sequence alignments, by using high ranked evolutionary couplings between residues as distance constraints to fold extended polypeptides. However, these methods, have not addressed the calculation of precise {beta}-sheet hydrogen bonding that defines transmembrane {beta}-barrels, and would be required to fold these proteins successfully. Hence we developed a method (EVFold_BB) that can successfully model transmembrane {beta}-barrels by combining evolutionary couplings together with topology predictions. EVFold_BB is validated by the accurate all-atom 3D modeling of 18 proteins, representing all known membrane {beta}-barrel families that have sufficient sequences available. To demonstrate the potential of our approach we predict the unknown 3D structure of the LptD protein, the plausibility of its accuracy is supported by the blindly predicted benchmarks, and is consistent with experimental observations. Our approach can naturally be extended to all unknown {beta}-barrel proteins with sufficient sequence information.

Bioinformatics↗

bioRxiv2014

svaseq: removing batch effects and other unwanted noise from sequencing data

It is now well known that unwanted noise and unmodeled artifacts such as batch effects can dramatically reduce the accuracy of statistical inference in genomic experiments. We introduced surrogate variable analysis for estimating these artifacts by (1) identifying the part of the genomic data only affected by artifacts and (2) estimating the artifacts with principal components or singular vectors of the subset of the data matrix. The resulting estimates of artifacts can be used in subsequent analyses as adjustment factors. Here I describe an update to the sva approach that can be applied to analyze count data or FPKMs from sequencing experiments. I also describe the addition of supervised sva (ssva) for using control probes to identify the part of the genomic data only affected by artifacts. These updates are available through the surrogate variable analysis (sva) Bioconductor package.

Bioinformatics↗

bioRxiv2014

Formalization of Genome Interval Relations

In order to take full advantage of next generation genomics data, I need informatics methods to be based on agreed upon formally specified standards that can be implemented easily in a uniform fashion without ambiguity. These standards should be encoded as logical formulae, so that provably correct and efficient decision procedures can be used for query answering and validation. In this paper I present the core of such a standard for sequence data: a collection of definitions of relations that hold between genomic intervals, and an alegbra for performing operations upon these intervals. I show how these relations can be used to extend formalize concepts in the Sequence Ontology (SO).

Bioinformatics↗

bioRxiv2014

Reconstructing subclonal composition and evolution from whole genome sequencing of tumors

Tumors often contain multiple, genetically distinct subpopulations of cancerous cells. These so-called subclonal populations are defined by distinct somatic mutations that include point mutations such as single nucleotide variants and small indels - collectively called simple somatic mutations (SSMs) - as well as larger structural changes that result in copy number variations (CNVs). In some cases, the genotype and prevalence of these subpopulations can be reconstructed based on high-throughput, short-read sequencing of DNA in one or more tumor samples. To date, no automated SSM-based subclonal reconstructions have been attempted on WGS data; and CNV-based reconstructions are limited to tumors with two or fewer cancerous subclonal populations and with a small number of CNVs.\n\nWe describe a new automated method, PhyloWGS, that can be applied to WGS data from one or more tumor samples to perform subclonal reconstruction based on both CNVs and SSMs. PhyloWGS successfully recovers the composition of mixtures of a highly rearranged TGCA cell line when a CNV-based method fails. On WGS data with average read depth of 40 from five time-series chronic lymphocytic leukemia samples, PhyloWGS recovers the same tumor phylogeny previously reconstructed using deep targeted resequencing. To further explore the limits of WGS-based subclonal reconstruction, we ran PhyloWGS on simulated data: PhyloWGS can reliably reconstruct as many as three cancerous subpopulations based on 30-50x coverage WGS data from a single tumor sample with 10s to 1000s of SSMs per subpopulation. At least five cancerous subpopulations can be reconstructed if provided with read depths of 200 or more.\n\nPhyloWGS is the first automated method that can be applied to WGS tumor data that accurately reconstructs the frequency, genotype and phylogeny of the subclonal populations based on both SSMs and CNVs. It also provides a principled, automated approach to combining overlapping SSM and CNV data. By demonstrating the utility of PhyloWGS on medium depth WGS data, including from examples with highly rearranged chromosomes, we have greatly expanded the range of tumors for which subclonal reconstruction is possible.

Bioinformatics↗

bioRxiv2014

Recurrent alternative splicing isoform switches in tumor samples provide novel signatures of cancer

Cancer genomics has been instrumental to determine the genetic alterations that are predictive of various tumor conditions. However, the majority of these alterations occur at low frequencies, motivating the need to expand the catalogue of cancer signatures. Alternative pre-mRNA splicing alterations, which bear major importance for the understanding of cancer, have not been exhaustively studied yet in the context of recent cancer genome projects. In this article we analyze RNA sequencing data for more than 4000 samples from The Cancer Genome Atlas (TCGA) project, including paired normal samples, to detect recurrent alternative splicing isoform switches in 9 different cancer types. We first investigate whether alternative splicing isoform changes are predictive of tumors by applying a rank-based algorithm based on the reversal of the relative expression of transcript isoforms. We find that consistent alternative splicing isoform changes can separate with high accuracy tumor and normal samples, as well as some cancer subtypes. We then searched for those changes that occur in the most abundant isoform, i.e isoform switches, and are therefore more likely to have a functional impact. In total we detected 244 isoform switches, which are associated to functional pathways that are frequently altered in cancer and also separate tumor and normal samples accurately. We further assessed whether these isoform changes are associated to somatic mutations. Surprisingly, only a few cases appear to have association, including the putative tumor suppressor FBLN2 and the tumor driver MYH11, which show association of an isoform switch to mutations and indels on the alternatively spliced exon. However, the number of observed mutations is in general not sufficient to explain the frequency of the found isoform switches, suggesting that recurrent isoform switching in cancer is mostly independent of somatic mutations. In summary, we present an effective approach to detect novel alternative splicing signatures that are predictive of tumors. Moreover, the same methodology has led to uncover recurrent isoform switches in tumors, which may provide novel prognostic and therapeutic targets.\n\nSoftware and data are available at: https://bitbucket.org/regulatorygenomicsupf/iso-ktsp and http://dx.doi.org/10.6084/m9.figshare.1061917

Bioinformatics↗

bioRxiv2014

Benchmark Analysis of Algorithms for Determining and Quantifying Full-length mRNA Splice Forms from RNA-Seq Data

The advantages of RNA sequencing (RNA-Seq) suggest it will replace microarrays for highly parallel gene expression analysis. For example, in contrast to arrays, RNA-Seq is expected to be able to provide accurate identification and quantification of full-length transcripts. A number of methods have been developed for this purpose, but short error prone reads makes it a difficult problem in practice. It is essential to determine which algorithms perform best, and where and why they fail. However, there is a dearth of independent and unbiased benchmarking studies of these algorithms. Here we take an approach using both simulated and experimental benchmark data to evaluate their accuracy. We conclude that most methods are inaccurate even using idealized data, and that no is method sufficiently accurate once complicating factors such as polymorphisms, intron signal, sequencing error, and multiple splice forms are present. These results point to the pressing need for further algorithm development.

Bioinformatics↗

bioRxiv2014

MINI REVIEW: Statistical methods for detecting differentially methylated loci and regions

DNA methylation, the reversible addition of methyl groups at CpG dinucleotides, represents an important regulatory layer associated with gene expression. Changed methylation status has been noted across diverse pathological states, including cancer. The rapid development and uptake of microarrays and large scale DNA sequencing has prompted an explosion of data analytic methods for processing and discovering changes in DNA methylation across varied data types. In this mini-review, we present a compact and accessible discussion of many of the salient challenges, such as experimental design, statistical methods for differential methylation detection, critical considerations such as cell type composition and the potential confounding that can arise from batch effects. From a statistical perspective, our main interests include the use of empirical Bayes or hierarchical models, which have proved immensely powerful in genomics, and the procedures by which false discovery control is achieved.

Bioinformatics↗

bioRxiv2014

Assessing allele specific expression across multiple tissues from RNA-seq read data

Motivation: RNA sequencing enables allele specific expression (ASE) studies that complement standard genotype expression studies for common variants and, importantly, also allow measuring the regulatory impact of rare variants. The Genotype-Tissue Expression project (GTEx) is collecting RNA-seq data on multiple tissues of a same set of individuals and novel methods are required for the analysis of these data. Results: We present a statistical method to compare different patterns of ASE across tissues and to classify genetic variants according to their impact on the tissue-wide expression profile. We focus on strong ASE effects that we are expecting to see for protein-truncating variants, but our method can also be adjusted for other types of ASE effects. We illustrate the method with a real data example on a tissue-wide expression profile of a variant causal for lipoid proteinosis, and with a simulation study to assess our method more generally. Availability: MAMBA software: http://birch.well.ox.ac.uk/~rivas/mamba/ R source code and data examples: http://www.iki.fi/mpirinen/ Contact: matti.pirinen@helsinki.fi rivas@well.ox.ac.uk

Bioinformatics↗

bioRxiv2014

Conservation of expression regulation throughout the animal kingdom

Following the increase in available sequenced genomes, tissue-specific transcriptomes are being determined for a rapidly growing number of highly diverse species. Traditionally, only the transcriptomes of related species with equivalent tissues have been compared. Such an analysis is much more challenging over larger evolutionary distances when complementary tissues cannot readily be defined. Here, we present a method for the cross-species mapping of tissue-specific and developmental gene expression patterns across a wide range of animals, including many non-model species. Our approach maps gene expression patterns between species without requiring the definition of homologous tissues. With the help of this mapping, gene expression patterns can be compared even across distantly related species. In our survey of 36 datasets across 27 species, we detected conserved expression programs on all taxonomic levels, both within animals and between the animals and their closest unicellular relatives, the choanoflagellates. We found that the rate of change in tissue expression patterns is a property of gene families. Our findings open new avenues of study for the comparison and transfer of knowledge between different species.

Bioinformatics↗

bioRxiv2014

Aligning sequence from molecular inversion probes

Summary: Molecular inversion probes (MIPs) allow efficient enrichment of genomic regions of interest for the purpose of targeted sequencing. To date, there is a paucity of simple-to-use software to align sequences derived from this method. Here, we describe a single program that performs mapping, arm removal, and deduplication before outputting alignments in SAM format. Availability: bwa-mips is available at https://github.com/brentp/bwa-mips under the MIT license. Contact: bpederse@gmail.com

Bioinformatics↗

bioRxiv2014

Rapid Core-Genome Alignment and Visualization for Thousands of Intraspecific Microbial Genomes

Though many microbial species or clades now have hundreds of sequenced genomes, existing whole-genome alignment methods do not efficiently handle comparisons on this scale. Here we present the Harvest suite of core-genome alignment and visualization tools for quickly analyzing thousands of intraspecific microbial strains. Harvest includes Parsnp, a fast core-genome multi-aligner, and Gingr, a dynamic visual platform. Combined they provide interactive core-genome alignments, variant calls, recombination detection, and phylogenetic trees. Using simulated and real data we demonstrate that our approach exhibits unrivaled speed while maintaining the accuracy of existing methods. The Harvest suite is open-source and freely available from: http://github.com/marbl/harvest.

Bioinformatics↗