ABC: software for interactive browsing of genomic multiple sequence alignment data

Background  

Alignment and comparison of related genome sequences is a powerful method to identify regions likely to contain functional elements. Such analyses are data intensive, requiring the inclusion of genomic multiple sequence alignments, sequence annotations, and scores describing regional attributes of columns in the alignment. Visualization and browsing of results can be difficult, and there are currently limited software options for performing this task.     

CLUSTAL V: improved software for multiple sequence alignment

The CLUSTAL package of multiple sequence alignment programshas been completely rewritten and many new features added. Thenew software is a single program called CLUSTAL V, which iswritten in C and can be used on standard C compiler. The mainnew features are the ability to store and reuse old alignmentsand the ability to calculate phylogenetic trees after alignment.The program is simple to use, completely menu driven and on-linehelp is provided.     

Clustal W—蛋白质与核酸序列分析软件

蛋白质与核酸的序列分析在现代生物学和生物信息学中发挥着重要作用,新的算法和软件层出不穷,本文介绍一个可运行在ＰＣ机上的完全免费的多序列比较软件－ＣｌｕｓｔａｌＷ,它不但可以进行蛋白质与核酸的多序列比较,分析不同序列之间的相似性关系,还可以绘制进化树。由于其灵活的输入输出格式、方便的参数设定和选择、详尽的在线帮助以及良好的可移植性,使得ＣｌｕｓｔａｌＷ在蛋白质与核酸的序列分析中得到了广泛应用。     

ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference

Highly divergent sites in multiple sequence alignments (MSAs), which can stem from erroneous inference of homology and saturation of substitutions, are thought to negatively impact phylogenetic inference. Thus, several different trimming strategies have been developed for identifying and removing these sites prior to phylogenetic inference. However, a recent study reported that doing so can worsen inference, underscoring the need for alternative alignment trimming strategies. Here, we introduce ClipKIT, an alignment trimming software that, rather than identifying and removing putatively phylogenetically uninformative sites, instead aims to identify and retain parsimony-informative sites, which are known to be phylogenetically informative. To test the efficacy of ClipKIT, we examined the accuracy and support of phylogenies inferred from 14 different alignment trimming strategies, including those implemented in ClipKIT, across nearly 140,000 alignments from a broad sampling of evolutionary histories. Phylogenies inferred from ClipKIT-trimmed alignments are accurate, robust, and time saving. Furthermore, ClipKIT consistently outperformed other trimming methods across diverse datasets, suggesting that strategies based on identifying and retaining parsimony-informative sites provide a robust framework for alignment trimming.

Highly divergent sites in multiple sequence alignments are thought to negatively impact phylogenetic inference; trimming methods aim to remove these sites, but recent analysis suggests that doing so can worsen inference. This study introduces ClipKIT, a trimming method that instead aims to retain parsimony-informative sites; phylogenetic inference using ClipKIT-trimmed alignments is accurate, robust and time-saving.     

GST-PRIME: a genome-wide primer design software for the generation of gene sequence tags

The availability of sequenced genomes has generated a need for experimental approaches that allow the simultaneous analysis of large, or even complete, sets of genes. To facilitate such analyses, we have developed GST-PRIME, a software package for retrieving and assembling gene sequences, even from complex genomes, using the NCBI public database, and then designing sets of primer pairs for use in gene amplification. Primers were designed by the program for the direct amplification of gene sequence tags (GSTs) from either genomic DNA or cDNA. Test runs of GST-PRIME on 2000 randomly selected Arabidopsis and Drosophila genes demonstrate that 93 and 88% of resulting GSTs, respectively, fulfilled imposed length criteria. GST-PRIME primer pairs were tested on a set of 1900 Arabidopsis genes coding for chloroplast-targeted proteins: 95% of the primer pairs used in PCRs with genomic DNA generated the correct amplicons. GST-PRIME can thus be reliably used for large-scale or specific amplification of intron-containing genes of multicellular eukaryotes.     

四种常用的生物序列比对软件比较

随着高通量测序技术的快速发展,下一代测序技术也迅速发展为生物领域中的主流技术,而理解下一代测序数据最重要的一步是比对。比对是进行后续生物信息分析的基石,也因此催生了很多比对软件。本文主要选取了四种常用的比对软件Bowtie2、BWA、MAQ和SOAP2,对这四种软件及算法进行综述,并通过实际测序数据对四种软件进行比较和评估,为生物学研究者选择最佳的短序列比对软件提供理论和实践依据。     

MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment

With its theoretical basis firmly established in molecular evolutionary and population genetics, the comparative DNA and protein sequence analysis plays a central role in reconstructing the evolutionary histories of species and multigene families, estimating rates of molecular evolution, and inferring the nature and extent of selective forces shaping the evolution of genes and genomes. The scope of these investigations has now expanded greatly owing to the development of high-throughput sequencing techniques and novel statistical and computational methods. These methods require easy-to-use computer programs. One such effort has been to produce Molecular Evolutionary Genetics Analysis (MEGA) software, with its focus on facilitating the exploration and analysis of the DNA and protein sequence variation from an evolutionary perspective. Currently in its third major release, MEGA3 contains facilities for automatic and manual sequence alignment, web-based mining of databases, inference of the phylogenetic trees, estimation of evolutionary distances and testing evolutionary hypotheses. This paper provides an overview of the statistical methods, computational tools, and visual exploration modules for data input and the results obtainable in MEGA.     

GLUE: a flexible software system for virus sequence data

Background

Virus 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.

Results

HCV-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.

Conclusion

GLUE 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.

     

GWAMA: software for genome-wide association meta-analysis

Background  

Despite the recent success of genome-wide association studies in identifying novel loci contributing effects to complex human traits, such as type 2 diabetes and obesity, much of the genetic component of variation in these phenotypes remains unexplained. One way to improving power to detect further novel loci is through meta-analysis of studies from the same population, increasing the sample size over any individual study. Although statistical software analysis packages incorporate routines for meta-analysis, they are ill equipped to meet the challenges of the scale and complexity of data generated in genome-wide association studies.     

Strategies for multiple sequence alignment

We present an overview of multiple sequence alignments to outline the practical consequences for the choices among different techniques and parameters. We begin with a discussion of the scoring methods for quantifying the quality of a multiple sequence alignment, followed by a discussion of the algorithms implemented within a variety of multiple sequence alignment programs. We also discuss additional alignment details such as gap penalty and distance metrics. The paper concludes with a discussion on how to improve alignment quality and the limitations of the techniques described in this paper     

An enhanced RNA alignment benchmark for sequence alignment programs

Background  

The performance of alignment programs is traditionally tested on sets of protein sequences, of which a reference alignment is known. Conclusions drawn from such protein benchmarks do not necessarily hold for the RNA alignment problem, as was demonstrated in the first RNA alignment benchmark published so far. For example, the twilight zone – the similarity range where alignment quality drops drastically – starts at 60 % for RNAs in comparison to 20 % for proteins. In this study we enhance the previous benchmark.     

Singular value decomposition analysis of protein sequence alignment score data.

One of the standard tools for the analysis of data arranged in matrix form is singular value decomposition (SVD). Few applications to genomic data have been reported to date mainly for the analysis of gene expression microarray data. We review SVD properties, examine mathematical terms and assumptions implicit in the SVD formalism, and show that SVD can be applied to the analysis of matrices representing pairwise alignment scores between large sets of protein sequences. In particular, we illustrate SVD capabilities for data dimension reduction and for clustering protein sequences. A comparison is performed between SVD-generated clusters of proteins and annotation reported in the SWISS-PROT Database for a set of protein sequences forming the calycin superfamily, entailing all entries corresponding to the lipocalin, cytosolic fatty acid-binding protein, and avidin-streptavidin Prosite patterns.     

Multiple sequence alignment

A method has been developed for aligning segments of several sequences at once. The number of search steps depends only polynomially on the number of sequences, instead of exponentially, because most alignments are rejected without being evaluated explicitly. A data structure herein called the "heap" facilitates this process. For a set of n sequence segments, the overall similarity is taken to be the sum of all the constituent segment pair similarities, which are in turn sums of corresponding residue similarity scores from a Table. The statistical models that test alignments for significance make it possible to group sequences objectively, even when most or all of the interrelationships are weak. These tests are very sensitive, while remaining quite conservative, and discourage the addition of "misfit" sequences to an existing set. The new techniques are applied to a set of five DNA-binding proteins, to a group of three enzymes that employ the coenzyme FAD, and to a control set. The alignment previously proposed for the DNA-binding proteins on the basis of structural comparisons and inspection of sequences is supported quite dramatically, and a highly significant alignment is found for the FAD-binding proteins.     

Contact-based sequence alignment

This paper introduces the novel method of contact-based protein sequence alignment, where structural information in the form of contact mutation probabilities is incorporated into an alignment routine using contact-mutation matrices (CAO: Contact Accepted mutatiOn). The contact-based alignment routine optimizes the score of matched contacts, which involves four (two per contact) instead of two residues per match in pairwise alignments. The first contact refers to a real side-chain contact in a template sequence with known structure, and the second contact is the equivalent putative contact of a homologous query sequence with unknown structure. An algorithm has been devised to perform a pairwise sequence alignment based on contact information. The contact scores were combined with PAM-type (Point Accepted Mutation) substitution scores after parameterization of gap penalties and score weights by means of a genetic algorithm. We show that owing to the structural information contained in the CAO matrices, significantly improved alignments of distantly related sequences can be obtained. This has allowed us to annotate eight putative Drosophila IGF sequences. Contact-based sequence alignment should therefore prove useful in comparative modelling and fold recognition.     

Multiple sequence alignment

Multiple sequence alignments are an essential tool for protein structure and function prediction, phylogeny inference and other common tasks in sequence analysis. Recently developed systems have advanced the state of the art with respect to accuracy, ability to scale to thousands of proteins and flexibility in comparing proteins that do not share the same domain architecture. New multiple alignment benchmark databases include PREFAB, SABMARK, OXBENCH and IRMBASE. Although CLUSTALW is still the most popular alignment tool to date, recent methods offer significantly better alignment quality and, in some cases, reduced computational cost.     

Homology-extended sequence alignment

We present a profile–profile multiple alignment strategy that uses database searching to collect homologues for each sequence in a given set, in order to enrich their available evolutionary information for the alignment. For each of the alignment sequences, the putative homologous sequences that score above a pre-defined threshold are incorporated into a position-specific pre-alignment profile. The enriched position-specific profile is used for standard progressive alignment, thereby more accurately describing the characteristic features of the given sequence set. We show that owing to the incorporation of the pre-alignment information into a standard progressive multiple alignment routine, the alignment quality between distant sequences increases significantly and outperforms state-of-the-art methods, such as T-COFFEE and MUSCLE. We also show that although entirely sequence-based, our novel strategy is better at aligning distant sequences when compared with a recent contact-based alignment method. Therefore, our pre-alignment profile strategy should be advantageous for applications that rely on high alignment accuracy such as local structure prediction, comparative modelling and threading.