Large-scale Sequence Alignment Research Articles

BSAlign: A Library for Nucleotide Sequence Alignment.

Increasing the accuracy of the nucleotide sequence alignment is an essential issue in genomics research. Although classic dynamic programming (DP) algorithms (e.g., Smith-Waterman and Needleman-Wunsch) guarantee to produce the optimal result, their time complexity hinders the application of large-scale sequence alignment. Many optimization efforts that aim to accelerate the alignment process generally come from three perspectives: redesigning data structures [e.g., diagonal or striped Single Instruction Multiple Data (SIMD) implementations], increasing the number of parallelisms in SIMD operations (e.g., difference recurrence relation), or reducing search space (e.g., banded DP). However, no methods combine all these three aspects to build an ultra-fast algorithm. In this study, we developed a Banded Striped Aligner (BSAlign) library that delivers accurate alignment results at an ultra-fast speed by knitting a series of novel methods together to take advantage of all of the aforementioned three perspectives with highlights such as active F-loop in striped vectorization and striped move in banded DP. We applied our new acceleration design on both regular and edit distance pairwise alignment. BSAlign achieved 2-fold speed-up than other SIMD-based implementations for regular pairwise alignment, and 1.5-fold to 4-fold speed-up in edit distance-based implementations for long reads. BSAlign is implemented in C programing language and is available at https://github.com/ruanjue/bsalign.

Optimization and Performance Analysis of CAT Method for DNA Sequence Similarity Searching and Alignment.

Bioinformatics is a rapidly developing field enabling scientific experiments via computer models and simulations. In recent years, there has been an extraordinary growth in biological databases. Therefore, it is extremely important to propose effective methods and algorithms for the fast and accurate processing of biological data. Sequence comparisons are the best way to investigate and understand the biological functions and evolutionary relationships between genes on the basis of the alignment of two or more DNA sequences in order to maximize the identity level and degree of similarity. This paper presents a new version of the pairwise DNA sequences alignment algorithm, based on a new method called CAT, where a dependency with a previous match and the closest neighbor are taken into consideration to increase the uniqueness of the CAT profile and to reduce possible collisions, i.e., two or more sequence with the same CAT profiles. This makes the proposed algorithm suitable for finding the exact match of a concrete DNA sequence in a large set of DNA data faster. In order to enable the usage of the profiles as sequence metadata, CAT profiles are generated once prior to data uploading to the database. The proposed algorithm consists of two main stages: CAT profile calculation depending on the chosen benchmark sequences and sequence comparison by using the calculated CAT profiles. Improvements in the generation of the CAT profiles are detailed and described in this paper. Block schemes, pseudo code tables, and figures were updated according to the proposed new version and experimental results. Experiments were carried out using the new version of the CAT method for DNA sequence alignment and different datasets. New experimental results regarding collisions, speed, and efficiency of the suggested new implementation are presented. Experiments related to the performance comparison with Needleman-Wunsch were re-executed with the new version of the algorithm to confirm that we have the same performance. A performance analysis of the proposed algorithm based on the CAT method against the Knuth-Morris-Pratt algorithm, which has a complexity of O(n) and is widely used for biological data searching, was performed. The impact of prior matching dependencies on uniqueness for generated CAT profiles is investigated. The experimental results from sequence alignment demonstrate that the proposed CAT method-based algorithm exhibits minimal deviation, which can be deemed negligible if such deviation is considered permissible in favor of enhanced performance. It should be noted that the performance of the CAT algorithm in terms of execution time remains stable, unaffected by the length of the analyzed sequences. Hence, the primary benefit of the suggested approach lies in its rapid processing capabilities in large-scale sequence alignment, a task that traditional exact algorithms would require significantly more time to perform.

Large scale sequence alignment via efficient inference in generative models

Finding alignments between millions of reads and genome sequences is crucial in computational biology. Since the standard alignment algorithm has a large computational cost, heuristics have been developed to speed up this task. Though orders of magnitude faster, these methods lack theoretical guarantees and often have low sensitivity especially when reads have many insertions, deletions, and mismatches relative to the genome. Here we develop a theoretically principled and efficient algorithm that has high sensitivity across a wide range of insertion, deletion, and mutation rates. We frame sequence alignment as an inference problem in a probabilistic model. Given a reference database of reads and a query read, we find the match that maximizes a log-likelihood ratio of a reference read and query read being generated jointly from a probabilistic model versus independent models. The brute force solution to this problem computes joint and independent probabilities between each query and reference pair, and its complexity grows linearly with database size. We introduce a bucketing strategy where reads with higher log-likelihood ratio are mapped to the same bucket with high probability. Experimental results show that our method is more accurate than the state-of-the-art approaches in aligning long-reads from Pacific Bioscience sequencers to genome sequences.

Met1-specific motifs conserved in OTUB subfamily of green plants enable rice OTUB1 to hydrolyse Met1 ubiquitin chains

Linear (Met1-linked) ubiquitination is involved inflammatory and innate immune signaling. Previous studies have characterized enzymes regulating the addition and removal of this modification in mammalian systems. However, only a few plant-derived deubiquitinases targeting Met1-linked ubiquitin chains have been reported and their mechanism of action remains elusive. Here, using a dehydroalanine-bearing Met1-diubiquitin suicide probe, we discover OTUB1 from Oryza sativa (OsOTUB1) as a Met1-linked ubiquitin chain-targeting deubiquitinase. By solving crystal structures of apo OsOTUB1 and an OsOTUB1/Met1-diubiquitin complex, we find that Met1 activity is conferred by Met1-specific motifs in the S1’ pocket of OsOTUB1. Large-scale sequence alignments and hydrolysis experiments provide evidence that these motifs are a general determinant of Met1 activity in the OTUB subfamily across species. Analysis of the species distribution of OTUBs capable of hydrolysing Met1-linked ubiquitin chains shows that this activity is conserved in green plants (Viridiplantae) and does not exist in metazoans, providing insights into the evolutionary differentiation between primitive plants and animals.

Proteins Binding to the Carbohydrate HNK-1: Common Origins?

The human natural killer (HNK-1) carbohydrate plays important roles during nervous system development, regeneration after trauma and synaptic plasticity. Four proteins have been identified as receptors for HNK-1: the laminin adhesion molecule, high-mobility group box 1 and 2 (also called amphoterin) and cadherin 2 (also called N-cadherin). Because of HNK-1′s importance, we asked whether additional receptors for HNK-1 exist and whether the four identified proteins share any similarity in their primary structures. A set of 40,000 sequences homologous to the known HNK-1 receptors was selected and used for large-scale sequence alignments and motif searches. Although there are conserved regions and highly conserved sites within each of these protein families, there was no sequence similarity or conserved sequence motifs found to be shared by all families. Since HNK-1 receptors have not been compared regarding binding constants and since it is not known whether the sulfated or non-sulfated part of HKN-1 represents the structurally crucial ligand, the receptors are more heterogeneous in primary structure than anticipated, possibly involving different receptor or ligand regions. We thus conclude that the primary protein structure may not be the sole determinant for a bona fide HNK-1 receptor, rendering receptor structure more complex than originally assumed.

Structural modeling and biophysical characterization of arginyl‐tRNA transferase (ATE1)

Arginylation is an important post-translational modification (PTM) defined by the non-ribosomal transfer of the amino acid arginine from an aminoacylated tRNA to a diverse set of polypeptide substrates. This reaction is catalyzed by arginyl-tRNA transferase (ATE1), a eukaryotic enzyme that is essential to maintain normal cellular homeostasis. Importantly, research has linked dysfunction of cellular arginylation to neurodegenerative diseases as well as cancer metastasis; however, little is known about ATE1's structure, interaction with its substrates, and its mechanism of action. To guide our understanding of ATE1's structure, function, and mechanism, we have determined a structural model of Saccharomyces cerevisiae ATE1 (ScATE1) and characterized this protein biophysically. Our results have revealed interesting similarities to, and differences from, the bacterial leucyl/phenylalanyl-tRNA transferase (L/F-transferase). Based on our structural predictions, we hypothesize that target protein recognition in ScATE1 follows a similar mechanism as L/F-transferases. Through large-scale sequence alignments, we have found a highly conserved, positively charged His residue cradled within the postulated enzyme active site, which we believe makes electrostatic contacts with the target polypeptide that is arginylated. While we hypothesize that transferase interactions with their respective aminoacyl-tRNAs are also comparable, we use electrophoretic mobility shift assays to determine that ATE1 binds uncharged tRNA weakly, suggesting a more complex interaction process than previously believed. Overall, these data provide a promising guide for further study into the structure, function, and mechanism of ATE1s.

Sequence Conservation and Structural Modeling of an Arginyl‐tRNA Transferase 1 (ATE1)

Arginyl-tRNATransferase 1 (ATE1) is a eukaryotic enzyme that arginylates cellular proteins, is an essential regulator of eukaryotic homeostasis via its involvement in the N-degron pathway, and regulates essential physiological processes such as embryogenesis, aging, cell migration, muscle contraction, and stress. Despite its importance, the structure, mechanism of action, and regulation of ATE1 has yet to be elucidated. The objective of this work is to model the three-dimensional fold of an ATE1, and to determine essential conserved residues that are involved in substrate recognition and enzymatic arginylation. In this study, we have modeled the structure of Saccharomyces cerevisiae ATE1 (ScATE1) and identified a putative location of the GCN5-related N-acetyl transferase (GNAT) fold, which is structurally conserved amongst amino-acid transferases. Additionally, we have performed a large-scale sequence alignment across eukaryotic ATE1s to map conserved residues onto our three-dimensional model. Notably, we have identified a highly-conserved His residue that would be consistent with the recognition of a negatively charged substrate and is located in a structural homologous site in bacterial amino-acid transferases. We hypothesize that this highly conserved (>90%) His residue is essential for substrate recognition, and site-directed mutagenesis is underway to verify this hypothesis. These results provide the first structural insights into the enzyme active site and mechanism of substrate recognition for ScATE1, and the strong sequence conservation suggest a common mode of action across eukaryotic homologs.

GLUE: a flexible software system for virus sequence data

BackgroundVirus genome sequences, generated in ever-higher volumes, can provide new scientific insights and inform our responses to epidemics and outbreaks. To facilitate interpretation, such data must be organised and processed within scalable computing resources that encapsulate virology expertise. GLUE (Genes Linked by Underlying Evolution) is a data-centric bioinformatics environment for building such resources. The GLUE core data schema organises sequence data along evolutionary lines, capturing not only nucleotide data but associated items such as alignments, genotype definitions, genome annotations and motifs. Its flexible design emphasises applicability to different viruses and to diverse needs within research, clinical or public health contexts.ResultsHCV-GLUE is a case study GLUE resource for hepatitis C virus (HCV). It includes an interactive public web application providing sequence analysis in the form of a maximum-likelihood-based genotyping method, antiviral resistance detection and graphical sequence visualisation. HCV sequence data from GenBank is categorised and stored in a large-scale sequence alignment which is accessible via web-based queries. Whereas this web resource provides a range of basic functionality, the underlying GLUE project can also be downloaded and extended by bioinformaticians addressing more advanced questions.ConclusionGLUE can be used to rapidly develop virus sequence data resources with public health, research and clinical applications. This streamlined approach, with its focus on reuse, will help realise the full value of virus sequence data.

Analysis of viral diversity for vaccine target discovery

BackgroundViral vaccine target discovery requires understanding the diversity of both the virus and the human immune system. The readily available and rapidly growing pool of viral sequence data in the public domain enable the identification and characterization of immune targets relevant to adaptive immunity. A systematic bioinformatics approach is necessary to facilitate the analysis of such large datasets for selection of potential candidate vaccine targets.ResultsThis work describes a computational methodology to achieve this analysis, with data of dengue, West Nile, hepatitis A, HIV-1, and influenza A viruses as examples. Our methodology has been implemented as an analytical pipeline that brings significant advancement to the field of reverse vaccinology, enabling systematic screening of known sequence data in nature for identification of vaccine targets. This includes key steps (i) comprehensive and extensive collection of sequence data of viral proteomes (the virome), (ii) data cleaning, (iii) large-scale sequence alignments, (iv) peptide entropy analysis, (v) intra- and inter-species variation analysis of conserved sequences, including human homology analysis, and (vi) functional and immunological relevance analysis.ConclusionThese steps are combined into the pipeline ensuring that a more refined process, as compared to a simple evolutionary conservation analysis, will facilitate a better selection of vaccine targets and their prioritization for subsequent experimental validation.

Flexible, fast and accurate sequence alignment profiling on GPGPU with PaSWAS.

MotivationTo obtain large-scale sequence alignments in a fast and flexible way is an important step in the analyses of next generation sequencing data. Applications based on the Smith-Waterman (SW) algorithm are often either not fast enough, limited to dedicated tasks or not sufficiently accurate due to statistical issues. Current SW implementations that run on graphics hardware do not report the alignment details necessary for further analysis.ResultsWith the Parallel SW Alignment Software (PaSWAS) it is possible (a) to have easy access to the computational power of NVIDIA-based general purpose graphics processing units (GPGPUs) to perform high-speed sequence alignments, and (b) retrieve relevant information such as score, number of gaps and mismatches. The software reports multiple hits per alignment. The added value of the new SW implementation is demonstrated with two test cases: (1) tag recovery in next generation sequence data and (2) isotype assignment within an immunoglobulin 454 sequence data set. Both cases show the usability and versatility of the new parallel Smith-Waterman implementation.

Crystal structures of Cg1458 reveal a catalytic lid domain and a common catalytic mechanism for the FAH family

Cg1458 was recently characterized as a novel soluble oxaloacetate decarboxylase. However, sequence alignment identified that Cg1458 has no similarity with other oxaloacetate decarboxylases and instead belongs to the FAH (fumarylacetoacetate hydrolase) family. Differences in the function of Cg1458 and other FAH proteins may suggest a different catalytic mechanism. To help elucidate the catalytic mechanism of Cg1458, crystal structures of Cg1458in both the open and closed conformations have been determined for the first time up to a resolution of 1.9 Å (1 Å=0.1nm) and 2.0 Å respectively. Comparison of both structures and detailed biochemical studies confirmed the presence of a catalytic lid domain which is missing in the native enzyme structure. In this lid domain, a glutamic acid-histidine dyad was found to be critical in mediating enzymatic catalysis. On the basis of structural modelling and comparison, as well as large-scale sequence alignment studies, we further determined that the catalytic mechanism of Cg1458 is actually through a glutamic acid-histidine-water triad, and this catalytic triad is common among FAH family proteins that catalyse the cleavage of the C-C bond of the substrate. Two sequence motifs, HxxE and Hxx…xxE have been identified as the basis for this mechanism.

A user-friendly web portal for T-Coffee on supercomputers

BackgroundParallel T-Coffee (PTC) was the first parallel implementation of the T-Coffee multiple sequence alignment tool. It is based on MPI and RMA mechanisms. Its purpose is to reduce the execution time of the large-scale sequence alignments. It can be run on distributed memory clusters allowing users to align data sets consisting of hundreds of proteins within a reasonable time. However, most of the potential users of this tool are not familiar with the use of grids or supercomputers.ResultsIn this paper we show how PTC can be easily deployed and controlled on a super computer architecture using a web portal developed using Rapid. Rapid is a tool for efficiently generating standardized portlets for a wide range of applications and the approach described here is generic enough to be applied to other applications, or to deploy PTC on different HPC environments.ConclusionsThe PTC portal allows users to upload a large number of sequences to be aligned by the parallel version of TC that cannot be aligned by a single machine due to memory and execution time constraints. The web portal provides a user-friendly solution.