Clustal is a series of widely used computer programs used in bioinformatics for multiple sequence alignment.[2] There have been many versions of Clustal over the development of the algorithm that are listed below. The analysis of each tool and its algorithm are also detailed in their respective categories. Available operating systems listed in the sidebar are a combination of the software availability and may not be supported for every current version of the Clustal tools. Clustal Omega has the widest variety of operating systems out of all the Clustal tools.
All variations of the Clustal software align sequences using a heuristic that progressively builds a multiple sequence alignment from a series of pairwise alignments. This method works by analyzing the sequences as a whole, then utilizing the UPGMA/Neighbor-joining method to generate a distance matrix. A guide tree is then calculated from the scores of the sequences in the matrix, then subsequently used to build the multiple sequence alignment by progressively aligning the sequences in order of similarity.[14] Essentially, Clustal creates multiple sequence alignments through three main steps:
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The original program in the Clustal series of software was developed in 1988 as a way to generate multiple sequence alignments on personal computers. ClustalV was released 4 years later and greatly improved upon the original, adding and altering a few key features, including a switch to being written in C instead of Fortran like its predecessor.
Both versions use the same fast approximate algorithm to calculate the similarity scores between sequences, which in turn produces the pairwise alignments. The algorithm works by calculating the similarity scores as the number of k-tuple matches between two sequences, accounting for a set penalty for gaps. The more similar the sequences, the higher the score, the more divergent, the lower the scores. Once the sequences are scored, a dendrogram is generated through the UPGMA to represent the ordering of the multiple sequence alignment. The higher ordered sets of sequences are aligned first, followed by the rest in descending order. The algorithm allows for very large data sets, and works fast. However, the speed is dependent on the range for the k-tuple matches chosen for the particular sequence type.[15]
Some of the most notable additions in ClustalV are profile alignments, and full command line interface options. The ability to use profile alignments allows the user to align two or more previous alignments or sequences to a new alignment and move misaligned sequences (low scored) further down the alignment order. This gives the user the option to gradually and methodically create multiple sequence alignments with more control than the basic option.[14] The option to run from the command line greatly expedites the multiple sequence alignment process. Sequences can be run with a simple command,
and the program will determine what type of sequence it is analyzing. When the program is completed, the output of the multiple sequence alignment as well as the dendrogram go to files with .aln and .dnd extensions respectively. The command line interface uses the default parameters, and doesn't allow for other options.[15]
ClustalW like the other Clustal tools is used for aligning multiple nucleotide or protein sequences in an efficient manner. It uses progressive alignment methods, which align the most similar sequences first and work their way down to the least similar sequences until a global alignment is created. ClustalW is a matrix-based algorithm, whereas tools like T-Coffee and Dialign are consistency-based. ClustalW has a fairly efficient algorithm that competes well against other software. This program requires three or more sequences in order to calculate a global alignment, for pairwise sequence alignment (2 sequences) use tools similar to EMBOSS, LALIGN.
ClustalΩ (alternatively written as Clustal O and Clustal Omega) is a fast and scalable program written in C and C++ used for multiple sequence alignment. It uses seeded guide trees and a new HMM engine that focuses on two profiles to generate these alignments.[19][20] The program requires three or more sequences in order to calculate the multiple sequence alignment, for two sequences use pairwise sequence alignment tools (EMBOSS, LALIGN). Clustal Omega is consistency-based and is widely viewed as one of the fastest online implementations of all multiple sequence alignment tools and still ranks high in accuracy, among both consistency-based and matrix-based algorithms.
Clustal Omega has five main steps in order to generate the multiple sequence alignment. The first is producing a pairwise alignment using the k-tuple method, also known as the word method. This, in summary, is a heuristic method that isn't guaranteed to find an optimal alignment solution, but is significantly more efficient than the dynamic programming method of alignment. After that, the sequences are clustered using the modified mBed method.[21] The mBed method calculates pairwise distance using sequence embedding. This step is followed by the k-means clustering method. Next, the guide tree is constructed using the UPGMA method. This is shown as multiple guide tree steps leading into one final guide tree construction because of the way the UPGMA algorithm works. At each step, (each diamond in the flowchart) the nearest two clusters are combined and is repeated until the final tree can be assessed. In the final step, the multiple sequence alignment is produced using HHAlign package from the HH-Suite, which uses two profile HMM's. A profile HMM is a linear state machine consisting of a series of nodes, each of which corresponds roughly to a position (column) in the alignment from which it was built.[22]
To construct multiple sequence alignments, we need to use varied heuristic methods. The computational complexity is O(2knk), where k is the number of sequences, and n is the length. In other words, to align eight DNA sequences 100 bases long each takes about 281008 = 31018 seconds, slightly longer than the estimated age of the universe.
The purpose of multiple sequence alignments can be sequence comparison, assessment of data quality, prediction of protein and RNA structures, database searching, and phylogenetic analysis. For this reason, varied methods are used depending on the purpose. We will have a more in-depth treatment of this topic in our upcoming tutorial.
There is an increasing demand to assemble and align large-scale biological sequence data sets. The commonly used multiple sequence alignment programs are still limited in their ability to handle very large amounts of sequences because the system lacks a scalable high-performance computing (HPC) environment with a greatly extended data storage capacity.
We designed ClustalXeed, a software system for multiple sequence alignment with incremental improvements over previous versions of the ClustalX and ClustalW-MPI software. The primary advantage of ClustalXeed over other multiple sequence alignment software is its ability to align a large family of protein or nucleic acid sequences. To solve the conventional memory-dependency problem, ClustalXeed uses both physical random access memory (RAM) and a distributed file-allocation system for distance matrix construction and pair-align computation. The computation efficiency of disk-storage system was markedly improved by implementing an efficient load-balancing algorithm, called "idle node-seeking task algorithm" (INSTA). The new editing option and the graphical user interface (GUI) provide ready access to a parallel-computing environment for users who seek fast and easy alignment of large DNA and protein sequence sets.
ClustalXeed can now compute a large volume of biological sequence data sets, which were not tractable in any other parallel or single MSA program. The main developments include: 1) the ability to tackle larger sequence alignment problems than possible with previous systems through markedly improved storage-handling capabilities. 2) Implementing an efficient task load-balancing algorithm, INSTA, which improves overall processing times for multiple sequence alignment with input sequences of non-uniform length. 3) Support for both single PC and distributed cluster systems.
Genetic and protein sequences are being discovered rapidly, and as a result, the number of sequences entered into biological databases is growing exponentially over time. Most of the work currently being done in computational biology involves searching for inter- and intra-sequence homology in massive volumes of genetic and protein sequence data, which are commonly based on a multiple sequence alignments (MSAs) [1]. However, increasing the computational efficiency to solve a variety of real MSA problems is still a challenging task because of the high demand for greater capacity and speed [2, 3].
The oldest and most widely used MSA program that estimates trees as it aligns multiple sequences is ClustalW [4, 5]. ClustalX is an integrated graphical-user-interface (GUI) version of the ClustalW multiple sequence alignment program [6]. It provides an easy-to-use work environment for performing MSA and pattern analyses. The latest version of ClustalX (version 2.0) added two new features [7]. The main advantage of ClustalX 2.0 is that it provides an easier way to maintain code for other applications. The new guided-tree implementation, compared with the older version, enables larger, faster computations. ClustalX 2.0 is now available for a number of platforms, including SUN Solaris, IRIX5.3 on Silicon Graphics, Digital UNIX on DECStations, Microsoft Windows for PCs, Linux ELF for x86 PCs, and Macintosh PowerMac.
Unfortunately, most of the currently available MSA programs are not suitable for large-capacity data storage and massive computation. These programs, including ClustalX (or X 2.0), are still single-PC based, and the storage and computation is entirely dependent on physical random-access memory (RAM). Past MSA performance evaluations focused simply on how compute-intense and sensitive the program was with respect to the longest-common-sequence (LCS)-based exact-string matching algorithm (e.g., the Smith-Waterman or Needleman-Wunsch method) [8]. Depending on both the volume of data to be aligned and the accuracy of the comparisons, computation using dynamic programming is extremely time-consuming when large sequence volumes and high accuracy are required simultaneously. Numerous parallel-computation programs, such as parallelized Praline [9], DiAlign P [10], ClustalW-MPI [11], and a commercial SGI parallel Clustal on a shared memory SGI multiprocessor [12], have been developed, primarily to increase computational speed, rather than for larger capacity data handling. Multiple sequence alignments with the Clustal series programs and the required features have been reviewed [13]. 2ff7e9595c
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