Feature Frequency Profile Research Articles

Biochemical Property Based Positional Matrix: A New Approach Towards Genome Sequence Comparison.

The growth of the genome sequence has become one of the emerging areas in the study of bioinformatics. It has led to an excessive demand for researchers to develop advanced methodologies for evolutionary relationships among species. The alignment-free methods have been proved to be more efficient and appropriate related to time and space than existing alignment-based methods for sequence analysis. In this study, a new alignment-free genome sequence comparison technique is proposed based on the biochemical properties of nucleotides. Each genome sequence can be distributed in four parameters to represent a 21-dimensional numerical descriptor using the Positional Matrix. To substantiate the proposed method, phylogenetic trees are constructed on the viral and mammalian datasets by applying the UPGMA/NJ clustering method. Further, the results of this method are compared with the results of the Feature Frequency Profiles method, the Positional Correlation Natural Vector method, the Graph-theoretic method, the Multiple Encoding Vector method, and the Fuzzy Integral Similarity method. In most cases, it is found that the present method produces more accurate results than the prior methods. Also, in the present method, the execution time for computation is comparatively small.

Positional Correlation Natural Vector: A Novel Method for Genome Comparison.

Advances in sequencing technology have made large amounts of biological data available. Evolutionary analysis of data such as DNA sequences is highly important in biological studies. As alignment methods are ineffective for analyzing large-scale data due to their inherently high costs, alignment-free methods have recently attracted attention in the field of bioinformatics. In this paper, we introduce a new positional correlation natural vector (PCNV) method that involves converting a DNA sequence into an 18-dimensional numerical feature vector. Using frequency and position correlation to represent the nucleotide distribution, it is possible to obtain a PCNV for a DNA sequence. This new numerical vector design uses six suitable features to characterize the correlation among nucleotide positions in sequences. PCNV is also very easy to compute and can be used for rapid genome comparison. To test our novel method, we performed phylogenetic analysis with several viral and bacterial genome datasets with PCNV. For comparison, an alignment-based method, Bayesian inference, and two alignment-free methods, feature frequency profile and natural vector, were performed using the same datasets. We found that the PCNV technique is fast and accurate when used for phylogenetic analysis and classification of viruses and bacteria.

Whole-proteome tree of life suggests a deep burst of organism diversity

An organism tree of life (organism ToL) is a conceptual and metaphorical tree to capture a simplified narrative of the evolutionary course and kinship among the extant organisms. Such a tree cannot be experimentally validated but may be reconstructed based on characteristics associated with the organisms. Since the whole-genome sequence of an organism is, at present, the most comprehensive descriptor of the organism, a whole-genome sequence-based ToL can be an empirically derivable surrogate for the organism ToL. However, experimentally determining the whole-genome sequences of many diverse organisms was practically impossible until recently. We have constructed three types of ToLs for diversely sampled organisms using the sequences of whole genome, of whole transcriptome, and of whole proteome. Of the three, whole-proteome sequence-based ToL (whole-proteome ToL), constructed by applying information theory-based feature frequency profile method, an "alignment-free" method, gave the most topologically stable ToL. Here, we describe the main features of a whole-proteome ToL for 4,023 species with known complete or almost complete genome sequences on grouping and kinship among the groups at deep evolutionary levels. The ToL reveals 1) all extant organisms of this study can be grouped into 2 "Supergroups," 6 "Major Groups," or 35+ "Groups"; 2) the order of emergence of the "founders" of all of the groups may be assigned on an evolutionary progression scale; 3) all of the founders of the groups have emerged in a "deep burst" at the very beginning period near the root of the ToL-an explosive birth of life's diversity.

Genome sequence comparison under a new form of tri-nucleotide representation based on bio-chemical properties of nucleotides

Genetic sequence analysis, classification of genome sequence and evolutionary relationship between species using their biological sequences, are the emerging research domain in Bioinformatics. Several methods have already been applied to DNA sequence comparison under tri-nucleotide representation. In this paper, a new form of tri-nucleotide representation is proposed for sequence comparison. The comparison does not depend on the alignment of the sequences. In this representation, the bio-chemical properties of the nucleotides are considered. The novelty of this method is that the sequences of unequal lengths are represented by vectors of the same length and each of the tri-nucleotide formed out of the given sequence has its unique representation. To validate the proposed method, it is verified on several data sets related to mammalians, viruses and bacteria. The results of this method are further compared with those obtained by methods such as probabilistic method, natural vector method, Fourier power spectrum method, multiple encoding vector method, and feature frequency profiles method. Moreover, this method produces accurate phylogeny in all the cases. It is also proved that the time complexity of the present method is less.

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

BackgroundAlthough software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods.ResultsWe propose a novel combination of supervised Machine Learning with Digital Signal Processing, resulting in ML-DSP: an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels. We test ML-DSP by classifying 7396 full mitochondrial genomes at various taxonomic levels, from kingdom to genus, with an average classification accuracy of >97%.A quantitative comparison with state-of-the-art classification software tools is performed, on two small benchmark datasets and one large 4322 vertebrate mtDNA genomes dataset. Our results show that ML-DSP overwhelmingly outperforms the alignment-based software MEGA7 (alignment with MUSCLE or CLUSTALW) in terms of processing time, while having comparable classification accuracies for small datasets and superior accuracies for the large dataset. Compared with the alignment-free software FFP (Feature Frequency Profile), ML-DSP has significantly better classification accuracy, and is overall faster.We also provide preliminary experiments indicating the potential of ML-DSP to be used for other datasets, by classifying 4271 complete dengue virus genomes into subtypes with 100% accuracy, and 4,710 bacterial genomes into phyla with 95.5% accuracy.Lastly, our analysis shows that the “Purine/Pyrimidine”, “Just-A” and “Real” numerical representations of DNA sequences outperform ten other such numerical representations used in the Digital Signal Processing literature for DNA classification purposes.ConclusionsDue to its superior classification accuracy, speed, and scalability to large datasets, ML-DSP is highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures and identifying their mechanistic determinants, and in evaluating genome integrity.

Phylogenetic analysis of integrases of Acinetobacter baumannii genomic islands

Acinetobacter baumannii among other pathogens is capable of acquiring new virulence determinants via mobile genetic elements (MGE). Analysis of the distribution of genomic islands (GIs) has shown in many species that most MGEs cluster tightly with specific lineages on a phylogenetic core genome tree. This study aims to address the questions on the association between GIs distribution among certain clonal lineagesand to test the contribution of G08 and G62 in A. baumannii to metal susceptibility phenotypes. The whole genome phylogenetic single nucleotide polymorphism (SNP) tree was build using the feature frequency profile. As input, the 101 complete A. baumannii genomes deposited in GenBank were used. The matrix was generated using command line blast and the R platform to generate the output. Susceptibility testing of heavy metals was performed on wild-type and GI-deletion mutants using eight different heavy metals. Whole genome phylogenetic SNP tree constructed on all complete deposited A. baumannii genomes showed that the integrase of the metal resistance G08 and G62 are present only in single or very few sequence types (STs). To test the hypothesis that these GI seven if present only in defined lineage are still mobile, G08 and G62 were selected respectively in strains AB0057 and ATCC17978. Phylogenetic analysis of the respective GI integrases confirmed that the G08int and G62int belong to a separate clade within the Acinetobacter tyrosine recombinases. Susceptibility testing in A. baumannii strain ATCC 17978, AB0057 and their respective ΔG62 and ΔG08 mutants confirmed the contribution of the GI-encoded efflux transporters to heavy metal decreased susceptibility.

Phylogenetic tree construction using trinucleotide usage profile (TUP).

BackgroundIt has been a challenging task to build a genome-wide phylogenetic tree for a large group of species containing a large number of genes with long nucleotides sequences. The most popular method, called feature frequency profile (FFP-k), finds the frequency distribution for all words of certain length k over the whole genome sequence using (overlapping) windows of the same length. For a satisfactory result, the recommended word length (k) ranges from 6 to 15 and it may not be a multiple of 3 (codon length). The total number of possible words needed for FFP-k can range from 46=4096 to 415.ResultsWe propose a simple improvement over the popular FFP method using only a typical word length of 3. A new method, called Trinucleotide Usage Profile (TUP), is proposed based only on the (relative) frequency distribution using non-overlapping windows of length 3. The total number of possible words needed for TUP is 43=64, which is much less than the total count for the recommended optimal “resolution” for FFP. To build a phylogenetic tree, we propose first representing each of the species by a TUP vector and then using an appropriate distance measure between pairs of the TUP vectors for the tree construction. In particular, we propose summarizing a DNA sequence by a matrix of three rows corresponding to three reading frames, recording the frequency distribution of the non-overlapping words of length 3 in each of the reading frame. We also provide a numerical measure for comparing trees constructed with various methods.ConclusionsCompared to the FFP method, our empirical study showed that the proposed TUP method is more capable of building phylogenetic trees with a stronger biological support. We further provide some justifications on this from the information theory viewpoint. Unlike the FFP method, the TUP method takes the advantage that the starting of the first reading frame is (usually) known. Without this information, the FFP method could only rely on the frequency distribution of overlapping words, which is the average (or mixture) of the frequency distributions of three possible reading frames. Consequently, we show (from the entropy viewpoint) that the FFP procedure could dilute important gene information and therefore provides less accurate classification.

Whole Genome Phylogeny of Bacillus by Feature Frequency Profiles (FFP)

Fifty complete Bacillus genome sequences and associated plasmids were compared using the “feature frequency profile” (FFP) method. The resulting whole-genome phylogeny supports the placement of three Bacillus species (B. thuringiensis, B. anthracis and B. cereus) as a single clade. The monophyletic status of B. anthracis was strongly supported by the analysis. FFP proved to be more effective in inferring the phylogeny of Bacillus than methods based on single gene sequences [16s rRNA gene, GryB (gyrase subunit B) and AroE (shikimate-5-dehydrogenase)] analyses. The findings of FFP analysis were verified using kSNP v2 (alignment-free sequence analysis method) and Harvest suite (core genome sequence alignment method).

Metabolic network motifs can provide novel insights into evolution: The evolutionary origin of Eukaryotic organelles as a case study

Phylogenetic trees are typically constructed using genetic and genomic data, and provide robust evolutionary relationships of species from the genomic point of view. We present an application of network motif mining and analysis of metabolic pathways that when used in combination with phylogenetic trees can provide a more complete picture of evolution. By using distributions of three-node motifs as a proxy for metabolic similarity, we analyze the ancestral origin of Eukaryotic organelles from the metabolic point of view to illustrate the application of our motif mining and analysis network approach. Our analysis suggests that the hypothesis of an early proto-Eukaryote could be valid. It also suggests that a δ- or ϵ-Proteobacteria may have been the endosymbiotic partner that gave rise to modern mitochondria. Our evolutionary analysis needs to be extended by building metabolic network reconstructions of species from the phylum Crenarchaeota, which is considered to be a possible archaeal ancestor of the eukaryotic cell. In this paper, we also propose a methodology for constructing phylogenetic trees that incorporates metabolic network signatures to identify regions of genomically-estimated phylogenies that may be spurious. We find that results generated from our approach are consistent with a parallel phylogenetic analysis using the method of feature frequency profiles.

Detecting Phylogenetic Signals in Eukaryotic Whole Genome Sequences

Whole genome sequences are a rich source of molecular data, with a potential for the discovery of novel evolutionary information. Yet, many parts of these sequences are not known to be under evolutionary pressure and, thus, are not conserved. Furthermore, a good model for whole genome evolution does not exist. Consequently, it is not a priori clear if a meaningful phylogenetic signal exists and can be extracted from the sequences as a whole. Indeed, very few phylogenies were reconstructed based on these sequences. Prior to this work, only two reconstruction methods were applied to large eukaryotic genomes: the K(r) method (Haubold et al., 2009), which was applied to genomes of rather small diversity (Drosophila species), and the feature frequency profile method (Sims et al., 2009a), which was applied to genomes of moderate diversity (mammals). We investigate the whole genome-based phylogenetic reconstruction question with respect to a much wider taxonomic sample. We apply K(r), FFP, and an alternative alignment-free method, the average common subsequence (ACS) (Ulitsky et al., 2006), to 24 multicellular eukaryotes (vertebrates, invertebrates, and plants). We also apply ACS to the proteome sequences of these 24 taxa. We compare the resulting trees to a standard reference, the National Center for Biotechnology Information (NCBI) taxonomy tree. Trees produced by ACS(AA), based on proteomes, are in complete agreement with the NCBI tree. For the genome-based reconstruction, ACS(DNA) produces trees whose agreement with the NCBI tree is excellent to very good for divergence times up to 800 million years ago, medium at 1 billion years ago, and poor at 1.6 billion years ago. We conclude that whole genomes do carry a clear phylogenetic signal, yet this signal "saturates" with longer divergence times. Furthermore, from the few existing methods, ACS is best capable of detecting this signal.

2011 German Escherichia coli O104:H4 outbreak: whole-genome phylogeny without alignment

A large-scale Escherichia coli O104:H4 outbreak occurred in Germany from May to July 2011, causing numerous cases of hemolytic-uremic syndrome (HUS) and deaths. Genomes of ten outbreak isolates and a historical O104:H4 strain isolated in 2001 were sequenced using different new generation sequencing platforms. Phylogenetic analyses were performed using various approaches which either are not genome-wide or may be subject to errors due to poor sequence alignment. Also, detailed pathogenicity analyses on the 2001 strain were not available. We reconstructed the phylogeny of E. coli using the genome-wide and alignment-free feature frequency profile method and revealed the 2001 strain to be the closest relative to the 2011 outbreak strain among all available E. coli strains at present and confirmed findings from previous alignment-based phylogenetic studies that the HUS-causing O104:H4 strains are more closely related to typical enteroaggregative E. coli (EAEC) than to enterohemorrhagic E. coli. Detailed re-examination of pathogenicity-related virulence factors and secreted proteins showed that the 2001 strain possesses virulence factors shared between typical EAEC and the 2011 outbreak strain. Our study represents the first attempt to elucidate the whole-genome phylogeny of the 2011 German outbreak using an alignment-free method, and suggested a direct line of ancestry leading from a putative EAEC-like ancestor through the 2001 strain to the 2011 outbreak strain.

2011 German Escherichia coli O104:H4 outbreak: Alignment-free whole-genome phylogeny by feature frequency profiles

AbstractAccuracy of SNP-based whole-genome phylogeny reconstruction relies heavily on quality of sequence alignment which is particularly hindered by poorly assembled genomes. Alignment-free methods might provide additional insights. Here, we constructed a whole-genome phylogeny of 10 isolates from the current German E. coli outbreak against 30 existing E. coli genomes as well as that of a historical EHEC isolate using the alignment-free feature frequency profile method. Our results revealed a high similarity among E. coli isolates from the current outbreak and the historical EHEC being the most closely related isolate sequenced thus far.

2011 German Escherichia coli O104:H4 outbreak: Alignment-free whole-genome phylogeny by feature frequency profiles

2011 German Escherichia coli O104:H4 outbreak: Alignment-free whole-genome phylogeny by feature frequency profiles

2011 German Escherichia coli outbreak: Alignment-free whole-genome phylogeny by feature frequency profiles

AbstractAccuracy of SNP-based whole-genome phylogeny reconstruction relies heavily on quality of sequence alignment which is particularly hindered by poorly assembled genomes. Alignment-free methods might provide additional insights. Here, we constructed a whole-genome phylogeny of 9 E.coli isolates from the 2011 German outbreak against existing E. coli genomes using the alignment-free feature frequency profile method. In addition, we looked for gene elements that distinguish the outbreak group from the other E. coli strains and possibly accounted for the emergence of the outbreak isolates using the distinguishing feature analysis.

Whole-genome phylogeny of Escherichia coli/Shigella group by feature frequency profiles (FFPs).

A whole-genome phylogeny of the Escherichia coli/Shigella group was constructed by using the feature frequency profile (FFP) method. This alignment-free approach uses the frequencies of l-mer features of whole genomes to infer phylogenic distances. We present two phylogenies that accentuate different aspects of E. coli/Shigella genomic evolution: (i) one based on the compositions of all possible features of length l = 24 (∼8.4 million features), which are likely to reveal the phenetic grouping and relationship among the organisms and (ii) the other based on the compositions of core features with low frequency and low variability (∼0.56 million features), which account for ∼69% of all commonly shared features among 38 taxa examined and are likely to have genome-wide lineal evolutionary signal. Shigella appears as a single clade when all possible features are used without filtering of noncore features. However, results using core features show that Shigella consists of at least two distantly related subclades, implying that the subclades evolved into a single clade because of a high degree of convergence influenced by mobile genetic elements and niche adaptation. In both FFP trees, the basal group of the E. coli/Shigella phylogeny is the B2 phylogroup, which contains primarily uropathogenic strains, suggesting that the E. coli/Shigella ancestor was likely a facultative or opportunistic pathogen. The extant commensal strains diverged relatively late and appear to be the result of reductive evolution of genomes. We also identify clade distinguishing features and their associated genomic regions within each phylogroup. Such features may provide useful information for understanding evolution of the groups and for quick diagnostic identification of each phylogroup.

Whole-proteome phylogeny of prokaryotes by feature frequency profiles: An alignment-free method with optimal feature resolution.

We present a whole-proteome phylogeny of prokaryotes constructed by comparing feature frequency profiles (FFPs) of whole proteomes. Features are l-mers of amino acids, and each organism is represented by a profile of frequencies of all features. The selection of feature length is critical in the FFP method, and we have developed a procedure for identifying the optimal feature lengths for inferring the phylogeny of prokaryotes, strictly speaking, a proteome phylogeny. Our FFP trees are constructed with whole proteomes of 884 prokaryotes, 16 unicellular eukaryotes, and 2 random sequences. To highlight the branching order of major groups, we present a simplified proteome FFP tree of monophyletic class or phylum with branch support. In our whole-proteome FFP trees (i) Archaea, Bacteria, Eukaryota, and a random sequence outgroup are clearly separated; (ii) Archaea and Bacteria form a sister group when rooted with random sequences; (iii) Planctomycetes, which possesses an intracellular membrane compartment, is placed at the basal position of the Bacteria domain; (iv) almost all groups are monophyletic in prokaryotes at most taxonomic levels, but many differences in the branching order of major groups are observed between our proteome FFP tree and trees built with other methods; and (v) previously "unclassified" genomes may be assigned to the most likely taxa. We describe notable similarities and differences between our FFP trees and those based on other methods in grouping and phylogeny of prokaryotes.

Whole-genome phylogeny of mammals: Evolutionary information in genic and nongenic regions

Ten complete mammalian genome sequences were compared by using the "feature frequency profile" (FFP) method of alignment-free comparison. This comparison technique reveals that the whole nongenic portion of mammalian genomes contains evolutionary information that is similar to their genic counterparts--the intron and exon regions. We partitioned the complete genomes of mammals (such as human, chimp, horse, and mouse) into their constituent nongenic, intronic, and exonic components. Phylogenic species trees were constructed for each individual component class of genome sequence data as well as the whole genomes by using standard tree-building algorithms with FFP distances. The phylogenies of the whole genomes and each of the component classes (exonic, intronic, and nongenic regions) have similar topologies, within the optimal feature length range, and all agree well with the evolutionary phylogeny based on a recent large dataset, multispecies, and multigene-based alignment. In the strictest sense, the FFP-based trees are genome phylogenies, not species phylogenies. However, the species phylogeny is highly related to the whole-genome phylogeny. Furthermore, our results reveal that the footprints of evolutionary history are spread throughout the entire length of the whole genome of an organism and are not limited to genes, introns, or short, highly conserved, nongenic sequences that can be adversely affected by factors (such as a choice of sequences, homoplasy, and different mutation rates) resulting in inconsistent species phylogenies.

Whole-proteome phylogeny of large dsDNA virus families by an alignment-free method

The vast sequence divergence among different virus groups has presented a great challenge to alignment-based sequence comparison among different virus families. Using an alignment-free comparison method, we construct the whole-proteome phylogeny for a population of viruses from 11 viral families comprising 142 large dsDNA eukaryote viruses. The method is based on the feature frequency profiles (FFP), where the length of the feature (l-mer) is selected to be optimal for phylogenomic inference. We observe that (i) the FFP phylogeny segregates the population into clades, the membership of each has remarkable agreement with current classification by the International Committee on the Taxonomy of Viruses, with one exception that the mimivirus joins the phycodnavirus family; (ii) the FFP tree detects potential evolutionary relationships among some viral families; (iii) the relative position of the 3 herpesvirus subfamilies in the FFP tree differs from gene alignment-based analysis; (iv) the FFP tree suggests the taxonomic positions of certain "unclassified" viruses; and (v) the FFP method identifies candidates for horizontal gene transfer between virus families.

Alignment-free genome comparison with feature frequency profiles (FFP) and optimal resolutions

For comparison of whole-genome (genic + nongenic) sequences, multiple sequence alignment of a few selected genes is not appropriate. One approach is to use an alignment-free method in which feature (or l-mer) frequency profiles (FFP) of whole genomes are used for comparison-a variation of a text or book comparison method, using word frequency profiles. In this approach it is critical to identify the optimal resolution range of l-mers for the given set of genomes compared. The optimum FFP method is applicable for comparing whole genomes or large genomic regions even when there are no common genes with high homology. We outline the method in 3 stages: (i) We first show how the optimal resolution range can be determined with English books which have been transformed into long character strings by removing all punctuation and spaces. (ii) Next, we test the robustness of the optimized FFP method at the nucleotide level, using a mutation model with a wide range of base substitutions and rearrangements. (iii) Finally, to illustrate the utility of the method, phylogenies are reconstructed from concatenated mammalian intronic genomes; the FFP derived intronic genome topologies for each l within the optimal range are all very similar. The topology agrees with the established mammalian phylogeny revealing that intron regions contain a similar level of phylogenic signal as do coding regions.

Local feature frequency profile: a method to measure structural similarity in proteins.

Measures of structural similarity between known protein structures provide an objective basis for classifying protein folds and for revealing a global view of the protein structure universe. Here, we describe a rapid method to measure structural similarity based on the profiles of representative local features of C(alpha) distance matrices of compared protein structures. We first extract a finite number of representative local feature (LF) patterns from the distance matrices of all protein fold families by medoid analysis. Then, each C(alpha) distance matrix of a protein structure is encoded by labeling all its submatrices by the index of the nearest representative LF patterns. Finally, the structure is represented by the frequency distribution of these indices, which we call the LF frequency (LFF) profile of the protein. The LFF profile allows one to calculate structural similarity scores among a large number of protein structures quickly, and also to construct and update the "map" of the protein structure universe easily. The LFF profile method efficiently maps complex protein structures into a common Euclidean space without prior assignment of secondary structure information or structural alignment.