Improve Alignment Accuracy Research Articles

How do alignment programs perform on sequencing data with varying qualities and from repetitive regions?

BackgroundNext-generation sequencing technologies generate a significant number of short reads that are utilized to address a variety of biological questions. However, quite often, sequencing reads tend to have low quality at the 3’ end and are generated from the repetitive regions of a genome. It is unclear how different alignment programs perform under these different cases. In order to investigate this question, we use both real data and simulated data with the above issues to evaluate the performance of four commonly used algorithms: SOAP2, Bowtie, BWA, and Novoalign.MethodsThe performance of different alignment algorithms are measured in terms of concordance between any pair of aligners (for real sequencing data without known truth) and the accuracy of simulated read alignment.ResultsOur results show that, for sequencing data with reads that have relatively good quality or that have had low quality bases trimmed off, all four alignment programs perform similarly. We have also demonstrated that trimming off low quality ends markedly increases the number of aligned reads and improves the consistency among different aligners as well, especially for low quality data. However, Novoalign is more sensitive to the improvement of data quality. Trimming off low quality ends significantly increases the concordance between Novoalign and other aligners. As for aligning reads from repetitive regions, our simulation data show that reads from repetitive regions tend to be aligned incorrectly, and suppressing reads with multiple hits can improve alignment accuracy.ConclusionsThis study provides a systematic comparison of commonly used alignment algorithms in the context of sequencing data with varying qualities and from repetitive regions. Our approach can be applied to different sequencing data sets generated from different platforms. It can also be utilized to study the performance of other alignment programs.

Accelerometer-Based, Portable Navigation vs Imageless, Large-Console Computer-Assisted Navigation in Total Knee Arthroplasty: A Comparison of Radiographic Results

Computer-assisted surgery (CAS) systems improve alignment accuracy in total knee arthroplasty (TKA) but have not been widely implemented. Eighty knees underwent TKA using an accelerometer-based, portable navigation device (KneeAlign 2; OrthAlign Inc, Aliso Viejo, California), and the radiographic results were compared with 80 knees performed using a large-console, imageless CAS system (AchieveCAS; Smith and Nephew, Memphis, Tennessee). In the KneeAlign 2 cohort, 92.5% of patients had an alignment within 3° of a neutral mechanical axis (vs 86.3% with AchieveCAS, P < .01), 96.2% had a tibial component alignment within 2° of perpendicular to tibial mechanical axis (vs 97.5% with AchieveCAS, P = .8), and 94.9% had a femoral component alignment within 2° of perpendicular to the femoral mechanical axis (vs 92.5% with AchieveCAS, P < .01). The mean tourniquet time in the KneeAlign 2 cohort was 48.1 ± 10.2 minutes vs 54.1 ± 10.5 minutes in the AchieveCAS cohort (P < .01). Accelerometer-based, portable navigation is as accurate as large-console, imageless CAS systems in TKA.

Improved Accuracy of Alignment With Patient-specific Positioning Guides Compared With Manual Instrumentation in TKA

Coronal malalignment occurs frequently in TKA and may affect implant durability and knee function. Designed to improve alignment accuracy and precision, the patient-specific positioning guide is predicated on restoration of the overall mechanical axis and is a multifaceted new tool in achieving traditional goals of TKA. We compared the effectiveness of patient-specific positioning guides to manual instrumentation with intramedullary femoral and extramedullary tibial guides in restoring the mechanical axis of the extremity and achieving neutral coronal alignment of the femoral and tibial components. We retrospectively reviewed 569 TKAs performed with patient-specific positioning guides and 155 with manual instrumentation by two surgeons using postoperative long-leg radiographs. For all patients, we assessed the zone in which the overall mechanical axis passed through the knee, and for one surgeon's cases (105 patient-specific positioning guide, 55 manual instrumentation), we also measured the hip-knee-ankle angle and the individual component angles with respect to their mechanical axes. The overall mechanical axis passed through the central third of the knee more often with patient-specific positioning guides (88%) than with manual instrumentation (78%). The overall mean hip-knee-ankle angle for patient-specific positioning guides (180.6°) was similar to manual instrumentation (181.1°), but there were fewer ± 3° hip-knee-ankle angle outliers with patient-specific positioning guides (9%) than with manual instrumentation (22%). The overall mean tibial (89.9° versus 90.4°) and femoral (90.7° versus 91.3°) component angles were closer to neutral with patient-specific positioning guides than with manual instrumentation, but the rate of ± 2° outliers was similar for both the tibia (10% versus 7%) and femur (22% versus 18%). Patient-specific positioning guides can assist in achieving a neutral mechanical axis with reduction in outliers.

Comparison of linear gap penalties and profile-based variable gap penalties in profile–profile alignments

Profile–profile alignment algorithms have proven powerful for recognizing remote homologs and generating alignments by effectively integrating sequence evolutionary information into scoring functions. In comparison to scoring function, the development of gap penalty functions has rarely been addressed in profile–profile alignment algorithms. Although indel frequency profiles have been used to construct profile-based variable gap penalties in some profile–profile alignment algorithms, there is still no fair comparison between variable gap penalties and traditional linear gap penalties to quantify the improvement of alignment accuracy. We compared two linear gap penalty functions, the traditional affine gap penalty (AGP) and the bilinear gap penalty (BGP), with two profile-based variable gap penalty functions, the Profile-based Gap Penalty used in SP5 (SPGP) and a new Weighted Profile-based Gap Penalty (WPGP) developed by us, on some well-established benchmark datasets. Our results show that profile-based variable gap penalties get limited improvements than linear gap penalties, whether incorporated with secondary structure information or not. Secondary structure information appears less powerful to be incorporated into gap penalties than into scoring functions. Analysis of gap length distributions indicates that gap penalties could stably maintain corresponding distributions of gap lengths in their alignments, but the distribution difference from reference alignments does not reflect the performance of gap penalties. There is useful information in indel frequency profiles, but it is still not good enough for improving alignment accuracy when used in profile-based variable gap penalties. All of the methods tested in this work are freely accessible at http://protein.cau.edu.cn/gppat/.

Robust Automatic Knee MR Slice Positioning Through Redundant and Hierarchical Anatomy Detection

Diagnostic magnetic resonance (MR) image quality is highly dependent on the position and orientation of the slice groups, due to the intrinsic high in-slice and low through-slice resolutions of MR imaging. Hence, the higher speed, accuracy, and reproducibility of automatic slice positioning, make it highly desirable over manual slice positioning. However, imaging artifacts, diseases, joint articulation, variations across ages and demographics as well as the extremely high performance requirements prevent state-of-the-art methods, such as volumetric registration, to be an off-the-shelf solution. In this paper, we address all these issues through an automatic slice positioning framework based on redundant and hierarchical learning. Our method has two hallmarks that are specifically designed to achieve high robustness and accuracy. 1) A redundant set of anatomy detectors are learned to provide local appearance cues. These detections are pruned and assembled according to a distributed anatomy model, which captures group-wise spatial configurations among anatomy primitives. This strategy brings about a high level of robustness and works even if a large portion of the target is distorted, missing, or occluded. 2) The detectors are learned and invoked in a hierarchical fashion, with each local detection scheduled and iterated according to its intrinsic invariance property. This iterative alignment process is shown to dramatically improve alignment accuracy. The proposed system is extensively validated on a large dataset including 744 clinical MR scans. Compared to state-of-the-art methods, our method exhibits superior performance in terms of robustness, accuracy, and reproducibility. The methodology is general and can be applied to other anatomies and other imaging modalities.

FAAST: Flow-space Assisted Alignment Search Tool

BackgroundHigh throughput pyrosequencing (454 sequencing) is the major sequencing platform for producing long read high throughput data. While most other sequencing techniques produce reading errors mainly comparable with substitutions, pyrosequencing produce errors mainly comparable with gaps. These errors are less efficiently detected by most conventional alignment programs and may produce inaccurate alignments.ResultsWe suggest a novel algorithm for calculating the optimal local alignment which utilises flowpeak information in order to improve alignment accuracy. Flowpeak information can be retained from a 454 sequencing run through interpretation of the binary SFF-file format. This novel algorithm has been implemented in a program named FAAST (Flow-space Assisted Alignment Search Tool).ConclusionsWe present and discuss the results of simulations that show that FAAST, through the use of the novel algorithm, can gain several percentage points of accuracy compared to Smith-Waterman-Gotoh alignments, depending on the 454 data quality. Furthermore, through an efficient multi-thread aware implementation, FAAST is able to perform these high quality alignments at high speed.The tool is available at http://www.ifm.liu.se/bioinfo/

Aligning short reads to reference alignments and trees

Likelihood-based methods for placing short read sequences from metagenomic samples into reference phylogenies have been recently introduced. At present, it is unclear how to align those reads with respect to the reference alignment that was deployed to infer the reference phylogeny. Moreover, the adaptability of such alignment methods with respect to the underlying reference alignment strategies/philosophies has not been explored. It has also not been assessed if the reference phylogeny can be deployed in conjunction with the reference alignment to improve alignment accuracy in this context. We assess different strategies for short read alignment and propose a novel phylogeny-aware alignment procedure. Our alignment method can improve the accuracy of subsequent phylogenetic placement of the reads into a reference phylogeny by up to 5.8 times compared with phylogeny-agnostic methods. It can be deployed to align reads to alignments generated by using fundamentally different alignment strategies (e.g. PRANK(+F) versus MUSCLE). http://www.exelixis-lab.org/software.html

A comprehensive benchmark study of multiple sequence alignment methods: current challenges and future perspectives.

Multiple comparison or alignmentof protein sequences has become a fundamental tool in many different domains in modern molecular biology, from evolutionary studies to prediction of 2D/3D structure, molecular function and inter-molecular interactions etc. By placing the sequence in the framework of the overall family, multiple alignments can be used to identify conserved features and to highlight differences or specificities. In this paper, we describe a comprehensive evaluation of many of the most popular methods for multiple sequence alignment (MSA), based on a new benchmark test set. The benchmark is designed to represent typical problems encountered when aligning the large protein sequence sets that result from today's high throughput biotechnologies. We show that alignmentmethods have significantly progressed and can now identify most of the shared sequence features that determine the broad molecular function(s) of a protein family, even for divergent sequences. However,we have identified a number of important challenges. First, the locally conserved regions, that reflect functional specificities or that modulate a protein's function in a given cellular context,are less well aligned. Second, motifs in natively disordered regions are often misaligned. Third, the badly predicted or fragmentary protein sequences, which make up a large proportion of today's databases, lead to a significant number of alignment errors. Based on this study, we demonstrate that the existing MSA methods can be exploited in combination to improve alignment accuracy, although novel approaches will still be needed to fully explore the most difficult regions. We then propose knowledge-enabled, dynamic solutions that will hopefully pave the way to enhanced alignment construction and exploitation in future evolutionary systems biology studies.

Incorporating Ab Initio energy into threading approaches for protein structure prediction

BackgroundNative structures of proteins are formed essentially due to the combining effects of local and distant (in the sense of sequence) interactions among residues. These interaction information are, explicitly or implicitly, encoded into the scoring function in protein structure prediction approaches—threading approaches usually measure an alignment in the sense that how well a sequence adopts an existing structure; while the energy functions in Ab Initio methods are designed to measure how likely a conformation is near-native. Encouraging progress has been observed in structure refinement where knowledge-based or physics-based potentials are designed to capture distant interactions. Thus, it is interesting to investigate whether distant interaction information captured by the Ab Initio energy function can be used to improve threading, especially for the weakly/distant homologous templates.ResultsIn this paper, we investigate the possibility to improve alignment-generating through incorporating distant interaction information into the alignment scoring function in a nontrivial approach. Specifically, the distant interaction information is introduced through employing an Ab Initio energy function to evaluate the “partial” decoy built from an alignment. Subsequently, a local search algorithm is utilized to optimize the scoring function.Experimental results demonstrate that with distant interaction items, the quality of generated alignments are improved on 68 out of 127 query-template pairs in Prosup benchmark. In addition, compared with state-to-art threading methods, our method performs better on alignment accuracy comparison.ConclusionsIncorporating Ab Initio energy functions into threading can greatly improve alignment accuracy.

Raptorx: Exploiting structure information for protein alignment by statistical inference

This work presents RaptorX, a statistical method for template-based protein modeling that improves alignment accuracy by exploiting structural information in a single or multiple templates. RaptorX consists of three major components: single-template threading, alignment quality prediction, and multiple-template threading. This work summarizes the methods used by RaptorX and presents its CASP9 result analysis, aiming to identify major bottlenecks with RaptorX and template-based modeling and hopefully directions for further study. Our results show that template structural information helps a lot with both single-template and multiple-template protein threading especially when closely-related templates are unavailable, and there is still large room for improvement in both alignment and template selection. The RaptorX web server is available at http://raptorx.uchicago.edu.

A philosophy-driven entity classification and enrichment for ontology mapping

Abstract: Ontologies are intended to facilitate semantic interoperability among distributed and intelligent information systems. Because of the distributed nature of the World Wide Web, Web ontologies have been developing in multiple forms of heterogeneity. For interoperating among information systems through heterogeneous ontologies, ontology mapping is a prerequisite process to generate alignment between two ontologies. In order to improve alignment accuracy, our approach is to clarify and enrich the semantics of ontological entities before mapping. For this purpose, we present a semi-automatic framework of entity classification and enrichment by applying three philosophical notions: identity condition, existential rigidity, and external dependency. Our objective is to supply a set of philosophy-driven anchors into ontologies for their mapping process by using a sortal taxonomy as a background knowledge model.

MSAProbs: multiple sequence alignment based on pair hidden Markov models and partition function posterior probabilities

Multiple sequence alignment is of central importance to bioinformatics and computational biology. Although a large number of algorithms for computing a multiple sequence alignment have been designed, the efficient computation of highly accurate multiple alignments is still a challenge. We present MSAProbs, a new and practical multiple alignment algorithm for protein sequences. The design of MSAProbs is based on a combination of pair hidden Markov models and partition functions to calculate posterior probabilities. Furthermore, two critical bioinformatics techniques, namely weighted probabilistic consistency transformation and weighted profile-profile alignment, are incorporated to improve alignment accuracy. Assessed using the popular benchmarks: BAliBASE, PREFAB, SABmark and OXBENCH, MSAProbs achieves statistically significant accuracy improvements over the existing top performing aligners, including ClustalW, MAFFT, MUSCLE, ProbCons and Probalign. Furthermore, MSAProbs is optimized for multi-core CPUs by employing a multi-threaded design, leading to a competitive execution time compared to other aligners. The source code of MSAProbs, written in C++, is freely and publicly available from http://msaprobs.sourceforge.net.

Plasma channel diagnostic based on laser centroid oscillations

A technique has been developed for measuring the properties of discharge-based plasma channels by monitoring the centroid location of a laser beam exiting the channel as a function of input alignment offset between the laser and the channel. Experiments were performed using low-intensity (&amp;lt;1014 W cm−2) laser pulses focused onto the entrance of a hydrogen-filled capillary discharge waveguide. Scanning the laser centroid position at the input of the channel and recording the exit position allow determination of the channel depth with an accuracy of a few percent, measurement of the transverse channel shape, and inference of the matched spot size. In addition, accurate alignment of the laser beam through the plasma channel is provided by minimizing laser centroid motion at the channel exit as the channel depth is scanned either by scanning the plasma density or the discharge timing. The improvement in alignment accuracy provided by this technique will be crucial for minimizing electron beam pointing errors in laser plasma accelerators.

Aligning Protein Sequences with Predicted Secondary Structure

Accurately aligning distant protein sequences is notoriously difficult. Since the amino acid sequence alone often does not provide enough information to obtain accurate alignments under the standard alignment scoring functions, a recent approach to improving alignment accuracy is to use additional information such as secondary structure. We make several advances in alignment of protein sequences annotated with predicted secondary structure: (1) more accurate models for scoring alignments, (2) efficient algorithms for optimal alignment under these models, and (3) improved learning criteria for setting model parameters through inverse alignment, as well as (4) in-depth experiments evaluating model variants on benchmark alignments. More specifically, the new models use secondary structure predictions and their confidences to modify the scoring of both substitutions and gaps. All models have efficient algorithms for optimal pairwise alignment that run in near-quadratic time. These models have many parameters, which are rigorously learned using inverse alignment under a new criterion that carefully balances score error and recovery error. We then evaluate these models by studying how accurately an optimal alignment under the model recovers benchmark reference alignments that are based on the known three-dimensional structures of the proteins. The experiments show that these new models provide a significant boost in accuracy over the standard model for distant sequences. The improvement for pairwise alignment is as much as 15% for sequences with less than 25% identity, while for multiple alignment the improvement is more than 20% for difficult benchmarks whose accuracy under standard tools is at most 40%.

New Automated Point-Cloud Alignment for Ground-Based Light Detection and Ranging Data of Long Coastal Sections

This paper presents new techniques with corresponding algorithms to automate three-dimensional point-cloud georeferencing for large-scale data sets collected in dynamic environments where typical controls cannot be efficiently employed. Beam distortion occurs at the scan window edges of long-range scans on near-linear surfaces from oblique laser reflections. Coregistration of adjacent scans relies on these overlapping edges, so alignment errors quickly propagate through the data set unless constraints (origin and leveling information) are incorporated throughout the alignment process. This new methodology implements these constraints with a multineighbor least-squares approach to simultaneously improve alignment accuracy between adjacent scans in a survey and between time-series surveys, which need to be aligned separately for quantitative change analysis. A 1.4-km test survey was aligned without the aforementioned constraints using global alignment techniques, and the modified scan origins showed poor ag...

CORAL: aligning conserved core regions across domain families

Motivation: Homologous protein families share highly conserved sequence and structure regions that are frequent targets for comparative analysis of related proteins and families. Many protein families, such as the curated domain families in the Conserved Domain Database (CDD), exhibit similar structural cores. To improve accuracy in aligning such protein families, we propose a profile–profile method CORAL that aligns individual core regions as gap-free units.Results: CORAL computes optimal local alignment of two profiles with heuristics to preserve continuity within core regions. We benchmarked its performance on curated domains in CDD, which have pre-defined core regions, against COMPASS, HHalign and PSI-BLAST, using structure superpositions and comprehensive curator-optimized alignments as standards of truth. CORAL improves alignment accuracy on core regions over general profile methods, returning a balanced score of 0.57 for over 80% of all domain families in CDD, compared with the highest balanced score of 0.45 from other methods. Further, CORAL provides E-values to aid in detecting homologous protein families and, by respecting block boundaries, produces alignments with improved ‘readability’ that facilitate manual refinement.Availability: CORAL will be included in future versions of the NCBI Cn3D/CDTree software, which can be downloaded at http://www.ncbi.nlm.nih.gov/Structure/cdtree/cdtree.shtml.Contact: fongj@ncbi.nlm.nih.gov.Supplementary information: Supplementary data are available at Bioinformatics online.

MICAlign: a sequence-to-structure alignment tool integrating multiple sources of information in conditional random fields

Sequence-to-structure alignment in template-based protein structure modeling for remote homologs remains a difficult problem even following the correct recognition of folds. Here we present MICAlign, a sequence-to-structure alignment tool that incorporates multiple sources of information from local structural contexts of template, sequence profiles, predicted secondary structures, solvent accessibilities, potential-like terms (including residue-residue contacts and solvent exposures) and pre-aligned structures and sequences. These features, together with a position-specific gap scheme, were integrated into conditional random fields through which the optimal parameters were automatically learned. MICAlign showed improved alignment accuracy over several other state-of-the-art alignment tools based on comparisons by using independent datasets. Freely available at (http://www.bioinfo.tsinghua.edu.cn/~xiaxf/micalign) for both web server and source code.

Local RNA structure alignment with incomplete sequence

Motivation: Accuracy of automated structural RNA alignment is improved by using models that consider not only primary sequence but also secondary structure information. However, current RNA structural alignment approaches tend to perform poorly on incomplete sequence fragments, such as single reads from metagenomic environmental surveys, because nucleotides that are expected to be base paired are missing.Results: We present a local RNA structural alignment algorithm, trCYK, for aligning and scoring incomplete sequences under a model using primary sequence conservation and secondary structure information when possible. The trCYK algorithm improves alignment accuracy and coverage of sequence fragments of structural RNAs in simulated metagenomic shotgun datasets.Availability: The source code for Infernal 1.0, which includes trCYK, is available at http://infernal.janelia.orgContact: kolbed@janelia.hhmi.org; eddys@janelia.hhmi.orgSupplementary information: Supplementary data are available at Bioinformatics online.

Multiple Sequence Alignment Based on Profile Alignment of Intermediate Sequences

Despite considerable efforts, it remains difficult to obtain accurate multiple sequence alignments. By using additional hits from database search of the input sequences, a few strategies have been proposed to significantly improve alignment accuracy, including the construction of profiles from the hits while performing profile alignment, the inclusion of high scoring hits into the input sequences, the use of intermediate sequence search to link distant homologs, and the use of secondary structure information. We develop an algorithm that integrates these strategies to further improve alignment accuracy by modifying the pair-Hidden Markov Model (HMM) approach in ProbCons to incorporate profiles of intermediate sequences from database search and utilize secondary structure predictions as in SPEM. We test our algorithm on a few sets of benchmark multiple alignments, including BAliBASE, HOMSTRAD, PREFAB, and SABmark, and show that it significantly outperforms MAFFT and ProbCons, which are among the best multiple alignment algorithms that do not utilize additional information, and SPEM, which is among the best multiple alignment algorithms that utilize additional hits from database search. The improvement in accuracy over SPEM can be as much as 5-10% when aligning divergent sequences. A software program that implements this approach (ISPAlign) is available at http://faculty.cs.tamu.edu/shsze/ispalign.

Improvement in Speed and Accuracy of Multiple Sequence Alignment Program PRIME

Multiple sequence alignment (MSA) is a useful tool in bioinformatics. Although many MSA algorithms have been developed, there is still room for improvement in accuracy and speed. We have developed an MSA program PRIME, whose crucial feature is the use of a group-to-group sequence alignment algorithm with a piecewise linear gap cost. We have shown that PRIME is one of the most accurate MSA programs currently available. However, PRIME is slower than other leading MSA programs. To improve computational performance, we newly incorporate anchoring and grouping heuristics into PRIME. An anchoring method is to locate well-conserved regions in a given MSA as anchor points to reduce the region of DP matrix to be examined, while a grouping method detects conserved subfamily alignments specified by phylogenetic tree in a given MSA to reduce the number of iterative refinement steps. The results of BAliBASE 3.0 and PREFAB 4 benchmark tests indicated that these heuristics contributed to reduction in the computational time of PRIME by more than 60% while the average alignment accuracy measures decreased by at most 2%. Additionally, we evaluated the effectiveness of iterative refinement algorithm based on maximal expected accuracy (MEA). Our experiments revealed that when many sequences are aligned, the MEA-based algorithm significantly improves alignment accuracy compared with the standard version of PRIME at the expense of a considerable increase in computation time.