Alignment Information Research Articles

Genome alignment with graph data structures: a comparison

BackgroundRecent advances in rapid, low-cost sequencing have opened up the opportunity to study complete genome sequences. The computational approach of multiple genome alignment allows investigation of evolutionarily related genomes in an integrated fashion, providing a basis for downstream analyses such as rearrangement studies and phylogenetic inference.Graphs have proven to be a powerful tool for coping with the complexity of genome-scale sequence alignments. The potential of graphs to intuitively represent all aspects of genome alignments led to the development of graph-based approaches for genome alignment. These approaches construct a graph from a set of local alignments, and derive a genome alignment through identification and removal of graph substructures that indicate errors in the alignment.ResultsWe compare the structures of commonly used graphs in terms of their abilities to represent alignment information. We describe how the graphs can be transformed into each other, and identify and classify graph substructures common to one or more graphs. Based on previous approaches, we compile a list of modifications that remove these substructures.ConclusionWe show that crucial pieces of alignment information, associated with inversions and duplications, are not visible in the structure of all graphs. If we neglect vertex or edge labels, the graphs differ in their information content. Still, many ideas are shared among all graph-based approaches. Based on these findings, we outline a conceptual framework for graph-based genome alignment that can assist in the development of future genome alignment tools.

SSW Library: An SIMD Smith-Waterman C/C++ Library for Use in Genomic Applications

BackgroundThe Smith-Waterman algorithm, which produces the optimal pairwise alignment between two sequences, is frequently used as a key component of fast heuristic read mapping and variation detection tools for next-generation sequencing data. Though various fast Smith-Waterman implementations are developed, they are either designed as monolithic protein database searching tools, which do not return detailed alignment, or are embedded into other tools. These issues make reusing these efficient Smith-Waterman implementations impractical.ResultsTo facilitate easy integration of the fast Single-Instruction-Multiple-Data Smith-Waterman algorithm into third-party software, we wrote a C/C++ library, which extends Farrar’s Striped Smith-Waterman (SSW) to return alignment information in addition to the optimal Smith-Waterman score. In this library we developed a new method to generate the full optimal alignment results and a suboptimal score in linear space at little cost of efficiency. This improvement makes the fast Single-Instruction-Multiple-Data Smith-Waterman become really useful in genomic applications. SSW is available both as a C/C++ software library, as well as a stand-alone alignment tool at: https://github.com/mengyao/Complete-Striped-Smith-Waterman-Library.ConclusionsThe SSW library has been used in the primary read mapping tool MOSAIK, the split-read mapping program SCISSORS, the MEI detector TANGRAM, and the read-overlap graph generation program RZMBLR. The speeds of the mentioned software are improved significantly by replacing their ordinary Smith-Waterman or banded Smith-Waterman module with the SSW Library.

C-Sibelia: an easy-to-use and highly accurate tool for bacterial genome comparison

We present C-Sibelia, a highly accurate and easy-to-use software tool for comparing two closely related bacterial genomes, which can be presented as either finished sequences or fragmented assemblies. C-Sibelia takes as input two FASTA files and produces: (1) a VCF file containing all identified single nucleotide variations and indels; (2) an XMFA file containing alignment information. The software also produces Circos diagrams visualizing high level genomic architecture for rearrangement analyses. C-Sibelia is a part of the Sibelia comparative genomics suite, which is freely available under the GNU GPL v.2 license at http://sourceforge.net/projects/sibelia-bio. C-Sibelia is compatible with Unix-like operating systems. A web-based version of the software is available at http://etool.me/software/csibelia.

C-Sibelia: an easy-to-use and highly accurate tool for bacterial genome comparison.

We present C-Sibelia, a highly accurate and easy-to-use software tool for comparing two closely related bacterial genomes, which can be presented as either finished sequences or fragmented assemblies. C-Sibelia takes as input two FASTA files and produces: (1) a VCF file containing all identified single nucleotide variations and indels; (2) an XMFA file containing alignment information. The software also produces Circos diagrams visualizing high level genomic architecture for rearrangement analyses. C-Sibelia is a part of the Sibelia comparative genomics suite, which is freely available under the GNU GPL v.2 license at http://sourceforge.net/projects/sibelia-bio. C-Sibelia is compatible with Unix-like operating systems. A web-based version of the software is available at http://etool.me/software/csibelia.

Wellington: a novel method for the accurate identification of digital genomic footprints from DNase-seq data

The expression of eukaryotic genes is regulated by cis-regulatory elements such as promoters and enhancers, which bind sequence-specific DNA-binding proteins. One of the great challenges in the gene regulation field is to characterise these elements. This involves the identification of transcription factor (TF) binding sites within regulatory elements that are occupied in a defined regulatory context. Digestion with DNase and the subsequent analysis of regions protected from cleavage (DNase footprinting) has for many years been used to identify specific binding sites occupied by TFs at individual cis-elements with high resolution. This methodology has recently been adapted for high-throughput sequencing (DNase-seq). In this study, we describe an imbalance in the DNA strand-specific alignment information of DNase-seq data surrounding protein–DNA interactions that allows accurate prediction of occupied TF binding sites. Our study introduces a novel algorithm, Wellington, which considers the imbalance in this strand-specific information to efficiently identify DNA footprints. This algorithm significantly enhances specificity by reducing the proportion of false positives and requires significantly fewer predictions than previously reported methods to recapitulate an equal amount of ChIP-seq data. We also provide an open-source software package, pyDNase, which implements the Wellington algorithm to interface with DNase-seq data and expedite analyses.

Maximum-entropy word alignment and posterior-based phrase extraction for machine translation

One of the fundamental assumptions in statistical machine translation (SMT) is that the correspondence between a sentence and its translation can be explained in terms of an alignment between their words. Such alignment information is typically not observed in the parallel corpora used to build the phrase table of an SMT system. Therefore, it is customary to estimate a probabilistic model of the assumed hidden word alignment, which is then used to extract bilingual phrase pairs. In standard extraction heuristics, the alignment model is under-exploited as the only information used from the posterior distribution is the Viterbi best alignment. This is due to the high computational complexity of the IBM models, which are the de facto standard for computing these alignments. Note that these models have other limitations, including their asymmetry and their inability to integrate rich, feature-based, descriptions. We argue that refining the word alignment model in a discriminative maximum-entropy framework substantially improves the alignment quality. We also show that these improved alignments combined with efficient and accurate computation of the link posterior distributions can also improve the overall translation performance, especially when applying posterior-based extraction methods.

Band alignment of two-dimensional transition metal dichalcogenides: Application in tunnel field effect transistors

Tunnel field effect transistors (TFETs) based on vertical stacking of two dimensional materials are of interest for low-power logic devices. The monolayer transition metal dichalcogenides (TMDs) with sizable band gaps show promise in building p-n junctions (couples) for TFET applications. Band alignment information is essential for realizing broken gap junctions with excellent electron tunneling efficiencies. Promising couples composed of monolayer TMDs are suggested to be VIB-MeX2 (Me = W, Mo; X = Te, Se) as the n-type source and IVB-MeX2 (Me = Zr, Hf; X = S, Se) as the p-type drain by density functional theory calculations.

ThreaDom: extracting protein domain boundary information from multiple threading alignments

Motivation: Protein domains are subunits that can fold and evolve independently. Identification of domain boundary locations is often the first step in protein folding and function annotations. Most of the current methods deduce domain boundaries by sequence-based analysis, which has low accuracy. There is no efficient method for predicting discontinuous domains that consist of segments from separated sequence regions. As template-based methods are most efficient for protein 3D structure modeling, combining multiple threading alignment information should increase the accuracy and reliability of computational domain predictions.Result: We developed a new protein domain predictor, ThreaDom, which deduces domain boundary locations based on multiple threading alignments. The core of the method development is the derivation of a domain conservation score that combines information from template domain structures and terminal and internal alignment gaps. Tested on 630 non-redundant sequences, without using homologous templates, ThreaDom generates correct single- and multi-domain classifications in 81% of cases, where 78% have the domain linker assigned within ±20 residues. In a second test on 486 proteins with discontinuous domains, ThreaDom achieves an average precision 84% and recall 65% in domain boundary prediction. Finally, ThreaDom was examined on 56 targets from CASP8 and had a domain overlap rate 73, 87 and 85% with the target for Free Modeling, Hard multiple-domain and discontinuous domain proteins, respectively, which are significantly higher than most domain predictors in the CASP8. Similar results were achieved on the targets from the most recently CASP9 and CASP10 experiments.Availability: http://zhanglab.ccmb.med.umich.edu/ThreaDom/.Contact: zhng@umich.eduSupplementary information: Supplementary data are available at Bioinformatics online.

PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif

Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapidly and accurately identifying plant protein SCLs without a machine learning algorithm. PlantLoc provides predicted SCLs results, confidence estimates and which is the substantiality motif and where it is located on the sequence. PlantLoc achieved the highest accuracy (overall accuracy of 80.8%) of identification of plant protein SCLs as benchmarked by using a new test dataset compared other plant SCL prediction webservers. The ability of PlantLoc to predict multiple sites was also significantly higher than for any other webserver. The predicted substantiality motifs of queries also have great potential for analysis of relationships with protein functional regions. The PlantLoc server is available at http://cal.tongji.edu.cn/PlantLoc/.

CoRAL: predicting non-coding RNAs from small RNA-sequencing data

The surprising observation that virtually the entire human genome is transcribed means we know little about the function of many emerging classes of RNAs, except their astounding diversities. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their abilities to classify the various collections of non-coding RNAs (ncRNAs). To address this, we developed Classification of RNAs by Analysis of Length (CoRAL), a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length and cleavage specificity to distinguish between different ncRNA populations. We evaluated CoRAL using genome-wide small RNA sequencing data sets from four human tissue types and were able to classify six different types of RNAs with ∼80% cross-validation accuracy. Analysis by CoRAL revealed that microRNAs, small nucleolar and transposon-derived RNAs are highly discernible and consistent across all human tissue types assessed, whereas long intergenic ncRNAs, small cytoplasmic RNAs and small nuclear RNAs show less consistent patterns. The ability to reliably annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using small RNA sequencing data in less well-characterized organisms.

Segment assembly, structure alignment and iterative simulation in protein structure prediction

Segment assembly, structure alignment and iterative simulation in protein structure prediction

RNA global alignment in the joint sequence–structure space using elastic shape analysis

The functions of RNAs, like proteins, are determined by their structures, which, in turn, are determined by their sequences. Comparison/alignment of RNA molecules provides an effective means to predict their functions and understand their evolutionary relationships. For RNA sequence alignment, most methods developed for protein and DNA sequence alignment can be directly applied. RNA 3-dimensional structure alignment, on the other hand, tends to be more difficult than protein structure alignment due to the lack of regular secondary structures as observed in proteins. Most of the existing RNA 3D structure alignment methods use only the backbone geometry and ignore the sequence information. Using both the sequence and backbone geometry information in RNA alignment may not only produce more accurate classification, but also deepen our understanding of the sequence–structure–function relationship of RNA molecules. In this study, we developed a new RNA alignment method based on elastic shape analysis (ESA). ESA treats RNA structures as three dimensional curves with sequence information encoded on additional dimensions so that the alignment can be performed in the joint sequence–structure space. The similarity between two RNA molecules is quantified by a formal distance, geodesic distance. Based on ESA, a rigorous mathematical framework can be built for RNA structure comparison. Means and covariances of full structures can be defined and computed, and probability distributions on spaces of such structures can be constructed for a group of RNAs. Our method was further applied to predict functions of RNA molecules and showed superior performance compared with previous methods when tested on benchmark datasets. The programs are available at http://stat.fsu.edu/ ∼jinfeng/ESA.html.

Rapid transfer alignment of laser SINS using quaternion based angular measurement

A novel rapid transfer alignment algorithm for laser strapdown inertial navigation system (SINS) is studied. Transfer alignment have typically relied on velocity measurements from the master SINS as the source of alignment information, but lever arm error must be compensated accurately while velocity information is utilized, all most every quaternion based error model is nonlinear, so nonlinear filtering algorithms are need, suffering from computational complex and large error. Aim at these problems, a novel improved rapid transfer alignment algorithm formulation is presented, applying quaternion to built the process and measurement models, the improvement employs a special manipulation of the measurement equation results in a linear pseudo-measurement equation, thus the classical linear Kalman filter is employed to estimate the state, need not lever arm error compensation, results in the reduce of computational burden. Observability analysis of this new transfer alignment algorithm has been done based on the piece-wise constant system (PWCS) method, results show that the presented algorithm can accomplish the initial alignment task perfectly. A transfer alignment simulation system is also developed for the evaluation and analysis of the presented algorithm, simulation results are confirmed with the theoretical conclusion, which can achieve the transfer alignment accuracy about 1mrad within 10s.

Identifying DNA motifs based on match and mismatch alignment information

The conventional way of identifying DNA motifs, solely based on match alignment information, is susceptible to a high number of spurious sites. A novel scoring system has been introduced by taking both match and mismatch alignment information into account. The mismatch alignment information is useful to remove spurious sites encountered in DNA motif searching. As an example, a correct TATA box site in Homo sapiens \(H4/g\) gene has successfully been identified based on match and mismatch alignment information.

Neurient: An algorithm for automatic tracing of confluent neuronal images to determine alignment

A goal of neural tissue engineering is the development and evaluation of materials that guide neuronal growth and alignment. However, the methods available to quantitatively evaluate the response of neurons to guidance materials are limited and/or expensive, and may require manual tracing to be performed by the researcher. We have developed an open source, automated Matlab-based algorithm, building on previously published methods, to trace and quantify alignment of fluorescent images of neurons in culture. The algorithm is divided into three phases, including computation of a lookup table which contains directional information for each image, location of a set of seed points which may lie along neurite centerlines, and tracing neurites starting with each seed point and indexing into the lookup table. This method was used to obtain quantitative alignment data for complex images of densely cultured neurons. Complete automation of tracing allows for unsupervised processing of large numbers of images. Following image processing with our algorithm, available metrics to quantify neurite alignment include angular histograms, percent of neurite segments in a given direction, and mean neurite angle. The alignment information obtained from traced images can be used to compare the response of neurons to a range of conditions. This tracing algorithm is freely available to the scientific community under the name Neurient, and its implementation in Matlab allows a wide range of researchers to use a standardized, open source method to quantitatively evaluate the alignment of dense neuronal cultures.

Functional Implications of Local DNA Structures in Regulatory Motifs

The three-dimensional structure of DNA has been proposed to be a major determinant for functional transcription factors (TFs) and DNA interaction. Here, we use hydroxyl radical cleavage pattern as a measure of local DNA structure. We compared the conservation between DNA sequence and structure in terms of information content and attempted to assess the functional implications of DNA structures in regulatory motifs. We used statistical methods to evaluate the structural divergence of substituting a single position within a binding site and applied them to a collection of putative regulatory motifs. The following are our major observations: (i) we observed more information in structural alignment than in the corresponding sequence alignment for most of the transcriptional factors; (ii) for each TF, majority of positions have more information in the structural alignment as compared to the sequence alignment; (iii) we further defined a DNA structural divergence score (SD score) for each wild-type and mutant pair that is distinguished by single-base mutation. The SD score for benign mutations is significantly lower than that of switch mutations. This indicates structural conservation is also important for TFBS to be functional and DNA structures will provide previously unappreciated information for TF to realize the binding specificity.

Predicting Long Non-coding RNAs Based on Genomic Sequence Information

The binary classification of coding and non-coding genes is simplified near to 50 years. Genome-wide transcriptome studies have revealed that there exist tens of thousands of long non-coding RNAs (lncRNAs), while the functions are being uncovered slowly. Accurate identification of lncRNAs is the initial step to the systematic characterization of lncRNAs. The diversity of transcription patterns for lncRNAs challenges the available non-coding RNA prediction algorithms. Until now, prediction of lncRNAs mostly relies on genomic sequence and cross-species alignment information. Here, we introduce the main strategies that can discriminate lncRNA from protein-coding transcripts. Especially, recently available machine learning algorithms are shown efficient to the rapid and accurate identification of lncRNAs from a large number of putative lncRNAs based on transcriptome assembled transcripts, which would provide the basis of understanding of lncRNA biology.

Operational Experience with the Frontier System in CMS

The Frontier framework is used in the CMS experiment at the LHC to deliver conditions data to processing clients worldwide, including calibration, alignment, and configuration information. Each central server at CERN, called a Frontier Launchpad, uses tomcat as a servlet container to establish the communication between clients and the central Oracle database. HTTP-proxy Squid servers, located close to clients, cache the responses to queries in order to provide high performance data access and to reduce the load on the central Oracle database. Each Frontier Launchpad also has its own reverse-proxy Squid for caching. The three central servers have been delivering about 5 million responses every day since the LHC startup, containing about 40 GB data in total, to more than one hundred Squid servers located worldwide, with an average response time on the order of 10 milliseconds. The Squid caches deployed worldwide process many more requests per day, over 700 million, and deliver over 40 TB of data. Several monitoring tools of the tomcat log files, the accesses of the Squids on the central Launchpad servers, and the availability of remote Squids have been developed to guarantee the performance of the service and make the system easily maintainable. Following a brief introduction of the Frontier framework, we describe the performance of this highly reliable and stable system, detail monitoring concerns and their deployment, and discuss the overall operational experience from the first two years of LHC data-taking.

도로 주행 시뮬레이션 평가를 통한 스마트 델리네이터의 안전운전 유도 효과분석

본 연구에서는 실시간으로 차량 및 도로교통상황에 대한 정보의 수집 및 전송이 가능한 스마트하이웨이의 도로 및 통신환경이 조성되었을 때를 가정하여, 도로 Infra 기반의 정보제공수단을 통해 전방 도로교통상태정보 및 안전정보를 다수의 운전자에게 공통적으로 제공하여 네트워크 차원의 안전운행을 유도할 수 있는 방안을 마련하고자 하였다. 기존의 델리네이터에서 위험도 등급에 따라 색깔을 달리해 표출하는 기능을 추가한 장비를 스마트 델리네이터(Smart Delineator)로 명명하였다. 스마트 델리네이터는 기존의 델리네이터의 기능과 같은 선형정보 제공 기능 뿐만 아니라, 시시각각 변화하는 도로교통 상황정보 및 노면 상태정보를 실시간으로 제공받아 전방 도로교통상황에 대해 제공된 경고정보를 운전자가 직관적으로 인지하고 이를 수용하여 충분한 여유거리와 시간을 갖고 대응할 수 있도록 안전정보를 제공하는 시설물이다. 운전자의 안전운전 유도에 미치는 영향을 살펴보고자 도로 주행 시뮬레이터를 이용한 모의 시뮬레이션 평가 결과와 만족도 설문조사를 통해 안전운전 유도 효과를 도출해 보았다. 도로 주행 시뮬레이션을 이용한 운전자 반응 평가 결과 스마트 델리네이터는 이벤트 발생 위치에 대한 판단이 가능하며, 사전에 규정된 위험도 등급에 따른 통행속도 감소율 또는 권장속도에 따라 운전자들의 행동을 유도하는 효과가 있었다. Assuming a completed Smart Highway road & communication environment that allows real-time information collection and transmission of road traffic condition ahead, the purpose of this study is to develop a plan for inducing a network-level safe driving pattern by providing road traffic condition and safety information to multiple drivers through a road information provision device. In this study, the device with a function that displays different colors according to the hazard level to the existing delineator has been named 'Smart Delineator'. Smart Delineator is a device that provides not only alignment information but also safety information for drivers to receive real-time warning information and intuitively recognize road traffic condition ahead so that drivers can respond. To examine the effects of safety driving inducement level on drivers, a simulation test was conducted using driving simulator as well as a satisfaction survey. The result showed that the Smart Delineator was able to identify the location of occurrence and affecting driving according pattern, either adhering to recommended speed or reducing speed according to the pre-defined hazard level.

Compression of next-generation sequencing reads aided by highly efficient de novo assembly

We present Quip, a lossless compression algorithm for next-generation sequencing data in the FASTQ and SAM/BAM formats. In addition to implementing reference-based compression, we have developed, to our knowledge, the first assembly-based compressor, using a novel de novo assembly algorithm. A probabilistic data structure is used to dramatically reduce the memory required by traditional de Bruijn graph assemblers, allowing millions of reads to be assembled very efficiently. Read sequences are then stored as positions within the assembled contigs. This is combined with statistical compression of read identifiers, quality scores, alignment information and sequences, effectively collapsing very large data sets to <15% of their original size with no loss of information. Availability: Quip is freely available under the 3-clause BSD license from http://cs.washington.edu/homes/dcjones/quip.