Short Read Alignment Research Articles

Reliably Detecting Clinically Important Variants Requires Both Combined Variant Calls and Optimized Filtering Strategies.

A diversity of tools is available for identification of variants from genome sequence data. Given the current complexity of incorporating external software into a genome analysis infrastructure, a tendency exists to rely on the results from a single tool alone. The quality of the output variant calls is highly variable however, depending on factors such as sequence library quality as well as the choice of short-read aligner, variant caller, and variant caller filtering strategy. Here we present a two-part study first using the high quality ‘genome in a bottle’ reference set to demonstrate the significant impact the choice of aligner, variant caller, and variant caller filtering strategy has on overall variant call quality and further how certain variant callers outperform others with increased sample contamination, an important consideration when analyzing sequenced cancer samples. This analysis confirms previous work showing that combining variant calls of multiple tools results in the best quality resultant variant set, for either specificity or sensitivity, depending on whether the intersection or union, of all variant calls is used respectively. Second, we analyze a melanoma cell line derived from a control lymphocyte sample to determine whether software choices affect the detection of clinically important melanoma risk-factor variants finding that only one of the three such variants is unanimously detected under all conditions. Finally, we describe a cogent strategy for implementing a clinical variant detection pipeline; a strategy that requires careful software selection, variant caller filtering optimizing, and combined variant calls in order to effectively minimize false negative variants. While implementing such features represents an increase in complexity and computation the results offer indisputable improvements in data quality.

A linear model for predicting performance of short-read aligners using genome complexity

Background The effectiveness and accuracy of aligning short reads to genomes have an important impact on many applications that rely on next-generation sequencing data. The computational requirements and material cost for aligning largescale short reads to genomes is also expensive. To prevent wasted time and resources for aligning short reads, we investigated the different measures of genome complexity [1] that correlated best to the performance of alignment to propose a linear model for each aligning method [2].

Tiling Assembly: a new tool for reference annotation-independent transcript assembly and novel gene identification by RNA-sequencing.

Annotation of the rice (Oryza sativa) genome has evolved significantly since release of its draft sequence, but it is far from complete. Several published transcript assembly programmes were tested on RNA-sequencing (RNA-seq) data to determine their effectiveness in identifying novel genes to improve the rice genome annotation. Cufflinks, a popular assembly software, did not identify all transcripts suggested by the RNA-seq data. Other assembly software was CPU intensive, lacked documentation, or lacked software updates. To overcome these shortcomings, a heuristic ab initio transcript assembly algorithm, Tiling Assembly, was developed to identify genes based on short read and junction alignment. Tiling Assembly was compared with Cufflinks to evaluate its gene-finding capabilities. Additionally, a pipeline was developed to eliminate false-positive gene identification due to noise or repetitive regions in the genome. By combining Tiling Assembly and Cufflinks, 767 unannotated genes were identified in the rice genome, demonstrating that combining both programmes proved highly efficient for novel gene identification. We also demonstrated that Tiling Assembly can accurately determine transcription start sites by comparing the Tiling Assembly genes with their corresponding full-length cDNA. We applied our pipeline to additional organisms and identified numerous unannotated genes, demonstrating that Tiling Assembly is an organism-independent tool for genome annotation.

Abstract 4856: ITD Assembler: An algorithm for internal tandem duplication discovery from short-read sequencing data

Abstract Tandem duplication of coding sequence is an important mechanism of somatic mutation capable of activating oncogenes such as fms-related tyrosine kinase 3 (FLT3) as well as the potential for underlying constitutional or germline disease. Detection of internal tandem duplication (ITD) in short-read sequencing data remains challenging due to deficiencies in short-read alignment where reads carrying signatures of ITD are often unmapped. Assembly based approaches collapse repetitive sequences, resulting in reduced or lost genomic complexity. The majority of structural variation detection tools are limited to finding insertions that are contained entirely within individual read alignments, and are unable to detect read-spanning duplications. Tools designed to detect large structural variation from paired-end reads that map discordantly based on expected fragment size and orientation do not provide the precision needed to detect ITDs. Only tools that consider the characteristic alignment features that signal the presence of ITDs will be able to detect them; these alignment features include: an excess of reads with unaligned, soft-clipped bases, read pairs where one end is unmapped located near the site of insertion, and read pairs where both ends are unmapped. We introduce ITD Assembler, a novel approach that searches all unmapped reads from exome capture or whole genome sequencing data to identify duplications. From the entire set of unmapped reads plus subset of mapped reads harboring soft-clipped bases, de Bruijn graph assembly is applied to select reads that form cycles, indicative of duplicated sequence structures. Reads from de Bruijn graph cycles are then assembled using an Overlap Layout Consensus (OLC) methodology thereby alleviating the collapse of repeat sequences affecting de Bruijn graph assembly approaches. Resulting OLC assembled contigs are locally aligned to the reference sequence to annotate the position of ITDs. ITD Assembler was run on The Cancer Genome Atlas (TCGA) acute myeloid leukemia (AML) dataset, and FLT3-ITD detection rates were compared against orthogonal algorithms. ITD Assembler identified the highest percentage of TCGA FLT3-ITDs, reported significantly higher allele fractions, and discovered additional ITDs in the KIT, CEBPA and WT1 cancer genes. Citation Format: Oliver A. Hampton, Navin Rustagi, Jie Li, Liu Xi, Richard A. Gibbs, Sharon E. Plon, Marek Kimmel, David A. Wheeler. ITD Assembler: An algorithm for internal tandem duplication discovery from short-read sequencing data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4856. doi:10.1158/1538-7445.AM2015-4856

ResSeq: Enhancing Short-Read Sequencing Alignment By Rescuing Error-Containing Reads.

Next-generation short-read sequencing is widely utilized in genomic studies. Biological applications require an alignment step to map sequencing reads to the reference genome, before acquiring expected genomic information. This requirement makes alignment accuracy a key factor for effective biological interpretation. Normally, when accounting for measurement errors and single nucleotide polymorphisms, short read mappings with a few mismatches are generally considered acceptable. However, to further improve the efficiency of short-read sequencing alignment, we propose a method to retrieve additional reliably aligned reads (reads with more than a pre-defined number of mismatches), using a Bayesian-based approach. In this method, we first retrieve the sequence context around the mismatched nucleotides within the already aligned reads; these loci contain the genomic features where sequencing errors occur. Then, using the derived pattern, we evaluate the remaining (typically discarded) reads with more than the allowed number of mismatches, and calculate a score that represents the probability that a specific alignment is correct. This strategy allows the extraction of more reliably aligned reads, therefore improving alignment sensitivity. The source code of our tool, ResSeq, can be downloaded from: https://github.com/hrbeubiocenter/Resseq.

MICA: A fast short-read aligner that takes full advantage of Many Integrated Core Architecture (MIC).

BackgroundShort-read aligners have recently gained a lot of speed by exploiting the massive parallelism of GPU. An uprising alterative to GPU is Intel MIC; supercomputers like Tianhe-2, currently top of TOP500, is built with 48,000 MIC boards to offer ~55 PFLOPS. The CPU-like architecture of MIC allows CPU-based software to be parallelized easily; however, the performance is often inferior to GPU counterparts as an MIC card contains only ~60 cores (while a GPU card typically has over a thousand cores).ResultsTo better utilize MIC-enabled computers for NGS data analysis, we developed a new short-read aligner MICA that is optimized in view of MIC's limitation and the extra parallelism inside each MIC core. By utilizing the 512-bit vector units in the MIC and implementing a new seeding strategy, experiments on aligning 150 bp paired-end reads show that MICA using one MIC card is 4.9 times faster than BWA-MEM (using 6 cores of a top-end CPU), and slightly faster than SOAP3-dp (using a GPU). Furthermore, MICA's simplicity allows very efficient scale-up when multiple MIC cards are used in a node (3 cards give a 14.1-fold speedup over BWA-MEM).SummaryMICA can be readily used by MIC-enabled supercomputers for production purpose. We have tested MICA on Tianhe-2 with 90 WGS samples (17.47 Tera-bases), which can be aligned in an hour using 400 nodes. MICA has impressive performance even though MIC is only in its initial stage of development.Availability and implementationMICA's source code is freely available at http://sourceforge.net/projects/mica-aligner under GPL v3.Supplementary informationSupplementary information is available as "Additional File 1". Datasets are available at www.bio8.cs.hku.hk/dataset/mica.

ContextMap 2: fast and accurate context-based RNA-seq mapping.

BackgroundMapping of short sequencing reads is a crucial step in the analysis of RNA sequencing (RNA-seq) data. ContextMap is an RNA-seq mapping algorithm that uses a context-based approach to identify the best alignment for each read and allows parallel mapping against several reference genomes.ResultsIn this article, we present ContextMap 2, a new and improved version of ContextMap. Its key novel features are: (i) a plug-in structure that allows easily integrating novel short read alignment programs with improved accuracy and runtime; (ii) context-based identification of insertions and deletions (indels); (iii) mapping of reads spanning an arbitrary number of exons and indels. ContextMap 2 using Bowtie, Bowtie 2 or BWA was evaluated on both simulated and real-life data from the recently published RGASP study.ConclusionsWe show that ContextMap 2 generally combines similar or higher recall compared to other state-of-the-art approaches with significantly higher precision in read placement and junction and indel prediction. Furthermore, runtime was significantly lower than for the best competing approaches. ContextMap 2 is freely available at http://www.bio.ifi.lmu.de/ContextMap.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0557-5) contains supplementary material, which is available to authorized users.

ClinSeK: a targeted variant characterization framework for clinical sequencing.

Applying genomics to patient care demands sensitive, unambiguous and rapid characterization of a known set of clinically relevant variants in patients’ samples, an objective substantially different from the standard discovery process, in which every base in every sequenced read must be examined. Further, the approach must be sufficiently robust as to be able to detect multiple and potentially rare variants from heterogeneous samples. To meet this critical objective, we developed a novel variant characterization framework, ClinSeK, which performs targeted analysis of relevant reads from high-throughput sequencing data. ClinSeK is designed for efficient targeted short read alignment and is capable of characterizing a wide spectrum of genetic variants from single nucleotide variation to large-scale genomic rearrangement breakpoints. Applying ClinSeK to over a thousand cancer patients demonstrated substantively better performance, in terms of accuracy, runtime and disk storage, for clinical applications than existing variant discovery tools. ClinSeK is freely available for academic use at http://bioinformatics.mdanderson.org/main/clinsek.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0155-1) contains supplementary material, which is available to authorized users.

Global and local sequence alignment with a bounded number of gaps

Pairwise sequence alignment techniques have gained renewed interest in recent years, primarily due to their applications in re-sequencing—the assembly of a genome directed by a reference sequence.In this article, we show that adding the flexibility of bounding the number of gaps inserted in an alignment strengthens the classical sequence alignment scheme of scoring matrices and affine gap penalty scores. We present GapsMis, an algorithm for pairwise global sequence alignment with a variable, but bounded, number of gaps. It is based on computing a variant of the traditional dynamic programming matrix for global sequence alignment. We also present GapsMis-L, the analogous algorithm for pairwise local sequence alignment with a variable, but bounded, number of gaps.To test the accuracy of GapsMis and GapsMis-L we performed millions of pairwise sequence alignments under realistic conditions, based on the properties of real full-length genomes. The results show that GapsMis and GapsMis-L can increase the accuracy of extending short-read alignments compared to the traditional approaches. The importance of our contribution is underlined by the fact that the provided algorithms may be seamlessly integrated into any biological pipeline. The open-source code of our implementation is freely available at http://www.inf.kcl.ac.uk/research/projects/gapmis/.

Introduction to the Special Issue on Cloud for Health

It is a pleasure to present this special issue covering subjects related to applying cloud computing to bioinformatics, biomedicine, and health, including solutions to area problems and architectural adaptation of cloud systems to fit those problems. The special issue includes papers selected from the workshops C4Bio 2014 and CCGrid-Health 2014 that, after further extension and additional review, were selected for publication. C4Bio 2014 and CCGrid-Health 2014 were held in Chicago, during May 26-29th 2014 within the framework of the 14th IEEE/ACM International Symposium on Cluster, and Grid Computing (CCGrid 2014). The special issue begins with the review paper Cloud Computing in Healthcare and Biomedicine. Calabrese and Cannataro explore the current academic and commercial cloud-based solutions developed in the healthcare and biomedicine domains, designed in order to satisfy the specific needs (e.g. massive scalable computing and storage, data sharing, on-demand access anytime and anywhere) in these fields. Subsequently, the paper summarizes main issues regarding the use of Computing in healthcare and biomedicine. The article Selected Approaches and Frameworks to Carry out Genomic Data Analysis on the Cloud, by Church and Goscinski, examines commonly used cloud-based genomic analysis services, introduces the approach of exposing data as services and proposes two new solutions, namely HPCaaS and Uncinus, which aim to automate service development, deployment process and data provision. By comparing and contrasting these solutions, the authors identify key mechanisms of service creation, execution and data access required to support non-computing specialists employing clouds. Ahmad, Hasan, and Siddique propose to formally reason about molecular pathways of Zsyntax based deduction using the HOL4 theorem prover and developing a biologist friendly graphical user interface. On the Formalization of Zsyntax with Applications in Molecular Biology shows additionally a use case with the formal analysis of cancer-related molecular pathway, i.e., TP53 degradation and metabolic pathway, known as Glycolysis. The article Accelerating Comparative Genomics Workflows in a Distributed Environment with Optimized Data Partiotioning and Workflow Fusion, by Choudhury et al., discusses different strategies for alleviating the computational constraints experienced by several data-intensive bioinformatics applications, presenting a workflow-based framework to parallelize the compute-intensive tasks of genome alignment and variant calling, the two most important stages in most comparative genomics applications. This framework provides workflow fusion to merge multiple sequential workflows into a single optimized workflow that benefits from caching, eliminating choke points, and stacked partitioning. In Performance Comparison and Tuning of Virtual Machines For Sequence Alignment Software, Estrada et al. explore the performance cost of virtualization for the fast growing application domain of genomics to inform the feasibility of running an NGS pipeline in a cloud, and in doing so consider two prevalent short-read sequence alignment programs, BWA and Novoalign. They execute these applications in three separate open-source system virtualization solutions (KVM hypervisor, Xen paravirtualized hypervisor, and Linux Containers) and compare the runtime in each environment against the runtime of the same system without virtualization and measure the relative performance of each hypervisor, presenting tuning suggestions for cloud implementors and users. The article Extending XNAT towards a Cloud-based Quality Assessment Platform for Retinal Optical Coherence Tomographies, by Jansen et al., presents a platform designed for automatic quality assessment of retinal OCTs and provided as a cloud-based service employing OpenStack. It extends the image management platform XNAT by services to calculate and store quality measures. It is also extensible regarding new quality measure algorithms, allowing the developer to upload, compile and test code for the system's architecture. Finally, the paper A Tool for Managing the X1.V1 Platform on the Cloud, by Marzini et al., proposes a framework for managing the execution of the X1.V1 platform on the Cloud. This framework enables an easy, quick, and secure management of the resources allocation and reallocation to X1.V1 Virtual Machines, in order to enhance the platform performance, optimize resource utilization and, consequently, reduce the whole services cost. We would like to thank all the reviewers for their effort, cooperation, and valuable feedback and all the authors who submitted papers to C4Bio 2014 and CCGrid-Health 2014 and to this Special Issue.

Performance Comparison and Tuning of Virtual Machines For Sequence Alignment Software

We explore the performance cost of virtualisation for the fast growing application domain of genomics. Traditionally, scientific applications have been considered too high-performance to pay the performance cost of virtualisation. However, as the demand for computing power for genomics is ever-increasing, the cloud can become an attractive way to meet the scaling challenge presented by Next-Generation Sequencing (NGS). We seek to explore the feasibility of running an NGS pipeline in a cloud, and in doing so consider two prevalent short-read sequence alignment programs, BWA and Novoalign. We executed those applications in three separate open-source system virtualisation solutions: the KVM hypervisor, the Xen para-virtualised hypervisor, and Linux Containers. We compare the runtime in each environment against the runtime of the same system without virtualisation and measure the relative performance of each hypervisor. We investigate and reduce as much as possible any overhead, presenting tuning suggestions for cloud implementers and users. Overall, we find that the overhead introduced by virtualisation can be reduced to low single-digit percentages, a cost we believe to be more than acceptable, especially given that two of the three solutions, Xen and Containers, exhibit near-zero overhead.

Processing Shotgun Proteomics Data on the Amazon Cloud with the Trans-Proteomic Pipeline

Cloud computing, where scalable, on-demand compute cycles and storage are available as a service, has the potential to accelerate mass spectrometry-based proteomics research by providing simple, expandable, and affordable large-scale computing to all laboratories regardless of location or information technology expertise. We present new cloud computing functionality for the Trans-Proteomic Pipeline, a free and open-source suite of tools for the processing and analysis of tandem mass spectrometry datasets. Enabled with Amazon Web Services cloud computing, the Trans-Proteomic Pipeline now accesses large scale computing resources, limited only by the available Amazon Web Services infrastructure, for all users. The Trans-Proteomic Pipeline runs in an environment fully hosted on Amazon Web Services, where all software and data reside on cloud resources to tackle large search studies. In addition, it can also be run on a local computer with computationally intensive tasks launched onto the Amazon Elastic Compute Cloud service to greatly decrease analysis times. We describe the new Trans-Proteomic Pipeline cloud service components, compare the relative performance and costs of various Elastic Compute Cloud service instance types, and present on-line tutorials that enable users to learn how to deploy cloud computing technology rapidly with the Trans-Proteomic Pipeline. We provide tools for estimating the necessary computing resources and costs given the scale of a job and demonstrate the use of cloud enabled Trans-Proteomic Pipeline by performing over 1100 tandem mass spectrometry files through four proteomic search engines in 9 h and at a very low cost.

Impacts of low coverage depths and post-mortem DNA damage on variant calling: a simulation study.

BackgroundMassively parallel sequencing platforms, featuring high throughput and relatively short read lengths, are well suited to ancient DNA (aDNA) studies. Variant identification from short-read alignment could be hindered, however, by low DNA concentrations common to historic samples, which constrain sequencing depths, and post-mortem DNA damage patterns.ResultsWe simulated pairs of sequences to act as reference and sample genomes at varied GC contents and divergence levels. Short-read sequence pools were generated from sample sequences, and subjected to varying levels of “post-mortem” damage by adjusting levels of fragmentation and fragmentation biases, transition rates at sequence ends, and sequencing depths. Mapping of sample read pools to reference sequences revealed several trends, including decreased alignment success with increased read length and decreased variant recovery with increased divergence. Variants were generally called with high accuracy, however identification of SNPs (single-nucleotide polymorphisms) was less accurate for high damage/low divergence samples. Modest increases in sequencing depth resulted in rapid gains in total variant recovery, and limited improvements to recovery of heterozygous variants.ConclusionsThis in silico study suggests aDNA-associated damage patterns minimally impact variant call accuracy and recovery from short-read alignment, while modest increases in sequencing depth can greatly improve variant recovery.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1219-8) contains supplementary material, which is available to authorized users.

A SURVEY ON NGS - SHORT READ ALIGNMENT IN HIGH PERFORMANCE COMPUTING

Next generation sequencing (NGS) is a new area for generating massive genome sequencing data at high speed with low cost. Due to this, there are huge problems for data storage, analysis, and management. At NGS, Short Read Alignment is a fundamental problem and it has a lot of applications, such as genetic variation, whole genome sequencing, and personalized medicine. Recently, many tools and algorithms have been developed to align the short-reads in NGS. This paper, describes a short survey on NSG-short read alignment in particular High Performance Computing environment. The authors discussed the performance of the most widely used tools like (BOWITE, BWA, SOAP, CLOUDBURST, etc.). Based on the results, CLOUDBURST gives the best performance compare to other methods.

Verification and validation of bioinformatics software without a gold standard: a case study of BWA and Bowtie.

BackgroundBioinformatics software quality assurance is essential in genomic medicine. Systematic verification and validation of bioinformatics software is difficult because it is often not possible to obtain a realistic "gold standard" for systematic evaluation. Here we apply a technique that originates from the software testing literature, namely Metamorphic Testing (MT), to systematically test three widely used short-read sequence alignment programs.ResultsMT alleviates the problems associated with the lack of gold standard by checking that the results from multiple executions of a program satisfy a set of expected or desirable properties that can be derived from the software specification or user expectations. We tested BWA, Bowtie and Bowtie2 using simulated data and one HapMap dataset. It is interesting to observe that multiple executions of the same aligner using slightly modified input FASTQ sequence file, such as after randomly re-ordering of the reads, may affect alignment results. Furthermore, we found that the list of variant calls can be affected unless strict quality control is applied during variant calling.ConclusionThorough testing of bioinformatics software is important in delivering clinical genomic medicine. This paper demonstrates a different framework to test a program that involves checking its properties, thus greatly expanding the number and repertoire of test cases we can apply in practice.

Bison: bisulfite alignment on nodes of a cluster.

BackgroundDNA methylation changes are associated with a wide array of biological processes. Bisulfite conversion of DNA followed by high-throughput sequencing is increasingly being used to assess genome-wide methylation at single-base resolution. The relative slowness of most commonly used aligners for processing such data introduces an unnecessarily long delay between receipt of raw data and statistical analysis. While this process can be sped-up by using computer clusters, current tools are not designed with them in mind and end-users must create such implementations themselves.ResultsHere, we present a novel BS-seq aligner, Bison, which exploits multiple nodes of a computer cluster to speed up this process and also has increased accuracy. Bison is accompanied by a variety of helper programs and scripts to ease, as much as possible, the process of quality control and preparing results for statistical analysis by a variety of popular R packages. Bison is also accompanied by bison_herd, a variant of Bison with the same output but that can scale to a semi-arbitrary number of nodes, with concomitant increased demands on the underlying message passing interface implementation.ConclusionsBison is a new bisulfite-converted short-read aligner providing end users easier scalability for performance gains, more accurate alignments, and a convenient pathway for quality controlling alignments and converting methylation calls into a form appropriate for statistical analysis. Bison and the more scalable bison_herd are natively able to utilize multiple nodes of a computer cluster simultaneously and serve to simplify to the process of creating analysis pipelines.

Abstract 3173: HPV integration and methylation patterns in cervical intraepithelial neoplasia and invasive cervical cancer via HPV capture and high-throughput sequencing

Abstract Persistent infection with high-risk human papillomaviruses (HPV) types is essential for the development of cervical cancer and its immediate precursor, cervical intraepithelial neoplasia 3 (CIN3). However, HPV infection alone is not sufficient to promote cervical carcinogenesis, thus it is hypothesized that the progression to invasive disease is dependent on viral and host factors. For example, it is well established that HPV integration into the host genome is a signature of invasive cervical cancer (ICC). Also, HPV genomes have CpG sites scattered throughout their genes that, with advancing disease, are increasingly methylated by the host cell's DNA methyltransferases. This methylation may alter the expression patterns of viral genes that are relevant to transformation. The goal of the current study was to investigate the landscape of viral-host integration and associated methylation in CIN and ICC. DNA from the lesions of patients diagnosed with either CIN or ICC was captured utilizing HPV and target gene enrichment and next-generation sequencing. DNA libraries were prepared using SureSelectXT Methyl-Seq Target Enrichment Kit (Agilent Technologies) and hybridized with a custom SureSelectXT Library (Agilent Technologies) designed to target 63 HPV types and xGen Lockdown Probes (Integrated DNA Technologies) designed to target 6 cancer-relevant genes. Library samples were pooled and sequenced using the Illumina HiSeq 2000. Bisulfate treated sequence reads obtained from Illumina Hi-Seq 2000 were analyzed by MethylCoder that generates per-base resolution of methylation data. In MethylCoder, the Genomic short-read nucleotide alignment program (GSNAP) was used for aligning short reads into reference genomes (HPV and hg19). For each CpG site, the methylation rate was calculated as the percentage of unconverted cytosines in each sample. In support of previous work, we found that multiple HPV types exhibited integration into various host genes in ICC. Also, HPV integration occurs in different patterns within the same gene. For example, in one tumor, HPV16 exhibited three distinct integrations into the PVT1 oncogene. Global methylation patterns were found to be unique among HPV types and samples, yet preliminary analysis suggests that the percent methylation in HPV may track with that of the host genome. Further, the overall methylation pattern of the virus was significantly different in CIN versus ICC. Here we provide a discussion of specific nonrandom integration sites across multiple ICC samples and representative HPV methylation profiles from CIN and ICC samples. Our data provide novel insights into the HPV CpG methylation sites and gene targets at viral-cellular junctions of different HPV types from CIN through ICC, thus potentially identifying new biomarkers for use in the screening and diagnosis of cervical cancer. Citation Format: Marissa Iden, Samantha Fye, Yi-Wen Huang, Pengyuan Liu, Janet S. Rader. HPV integration and methylation patterns in cervical intraepithelial neoplasia and invasive cervical cancer via HPV capture and high-throughput sequencing. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3173. doi:10.1158/1538-7445.AM2014-3173

Alignment of short reads to multiple genomes using hashing

Materials and methods Inspired by [3], we propose a new method that attempts to take advantage of multiple genomes and SNV information to align reads. This approach is promising in that it allows us to distinguish between sequencing errors and SNV. Our proposed alignment algorithm uses read fragments to identify seeds and extend these seeds to find occurrences of reads in the genome. In this study, we have developed and implemented an algorithm using multiple genomes that captures genomic variations, indexes the multiple genomes and operates short read alignment on a collection of genomes. The preliminary result was validated on Aspergillus fumigatus.

RNA-Seq alignment to individualized genomes improves transcript abundance estimates in multiparent populations.

Massively parallel RNA sequencing (RNA-seq) has yielded a wealth of new insights into transcriptional regulation. A first step in the analysis of RNA-seq data is the alignment of short sequence reads to a common reference genome or transcriptome. Genetic variants that distinguish individual genomes from the reference sequence can cause reads to be misaligned, resulting in biased estimates of transcript abundance. Fine-tuning of read alignment algorithms does not correct this problem. We have developed Seqnature software to construct individualized diploid genomes and transcriptomes for multiparent populations and have implemented a complete analysis pipeline that incorporates other existing software tools. We demonstrate in simulated and real data sets that alignment to individualized transcriptomes increases read mapping accuracy, improves estimation of transcript abundance, and enables the direct estimation of allele-specific expression. Moreover, when applied to expression QTL mapping we find that our individualized alignment strategy corrects false-positive linkage signals and unmasks hidden associations. We recommend the use of individualized diploid genomes over reference sequence alignment for all applications of high-throughput sequencing technology in genetically diverse populations.

PyBamView: a browser-based application for viewing short read alignments.

Current sequence alignment browsers allow visualization of large and complex next-generation sequencing datasets. However, most of these tools provide inadequate display of insertions and can be cumbersome to use on large datasets. I implemented PyBamView, a lightweight Web application for visualizing short read alignments. It provides an easy-to-use Web interface for viewing alignments across multiple samples, with a focus on accurate visualization of insertions. PyBamView is available as a standard python package. The source code is freely available under the MIT license at https://mgymrek.github.io/pybamview.