Sequence Comparison Methods Research Articles

Improved functions for nonlinear sequence comparison using SEEKR.

SEquence Evaluation through k-mer Representation (SEEKR) is a method of sequence comparison that uses sequence substrings called k-mers to quantify the nonlinear similarity between nucleic acid species. We describe the development of new functions within SEEKR that enable end-users to estimate P-values that ascribe statistical significance to SEEKR-derived similarities, as well as visualize different aspects of k-mer similarity. We apply the new functions to identify chromatin-enriched lncRNAs that contain XIST-like sequence features, and we demonstrate the utility of applying SEEKR on lncRNA fragments to identify potential RNA-protein interaction domains. We also highlight ways in which SEEKR can be applied to augment studies of lncRNA conservation, and we outline the best practice of visualizing RNA-seq read density to evaluate support for lncRNA annotations before their in-depth study in cell types of interest.

PhamClust: a phage genome clustering tool using proteomic equivalence.

Bacteriophage comparative genomics is complex due to the mosaic nature of the genomes, and an underlying continuum of diversity confounds the identification of clear taxonomic boundaries. Nucleotide sequence comparison methods have been described for phage taxonomy, but they are computationally intensive and scale poorly as the number of sequenced phage genomes increases. Here, we describe PhamClust as a new bioinformatic approach for grouping phages according to their inter-genome relatedness. PhamClust calculates a proteomic equivalence quotient (PEQ) for each pair of phages based on amino acid sequence identity for those genes that are shared among phages. PEQ values span from 0% (no shared genes) to 100% (all genes shared at 100% identity), and using a large mycobacteriophage genome data set, we show that two-step clustering down to a PEQ of 25% constructs genome groupings (clusters) closely mirroring those constructed manually over time, with the differences attributable to historically arising incongruities rather than illogicalities in PhamClust. PEQ values can also faithfully divide clusters into subclusters, although the relationships are highly heterogeneous, with different PEQ values needed for the subdivision of different clusters. PhamClust can be used to assort any group of phages, including the RefSeq phage collection. IMPORTANCE Bacteriophage genomes are pervasively mosaic, presenting challenges to describing phage relatedness. Here, we describe PhamClust, a bioinformatic approach for phage genome comparisons that uses a new metric of proteomic equivalence quotient for comparative genomics. PhamClust reliably assorts genomes into groups or clusters of related phages and can subdivide clusters into subclusters. PhamClust is computationally efficient and can readily process thousands of phage genomes. It is also a useful analytic tool for exploring the different types of inter-genome relatedness characteristic of phages in different clusters.

Protein Sequence Comparison Method Based on 3-ary Huffman Coding

Based on 3-ary Huffman coding algorithm, we propose a digital mapping method of protein sequence. Firstly, a 3-ary Huffman tree is defined by the frequency characteristic of 20 amino acids in given protein sequences. The 0-2 codes of 20 amino acids constructed by the 3-ary Huffman tree can convert long protein sequences into one-to-one 0-2 digital sequences. According to the frequency characteristic and the distribution information of 0-2 codes of 20 amino acids in the 0-2 digital sequences, we design the 40-dimensional vectors to characterize the protein sequences. Next, the proposed digital mapping method is used to perform three separate applications, similarity comparison of nine ND6 proteins, evolutionary trend analysis of the 2009 pandemic Human influenza A (H1N1) viruses from January 2020 to June 2022, and the evolution analysis of 95 coronavirus genes. The results illustrate the utility of the proposed method.

How to optimally sample a sequence for rapid analysis.

We face an increasing flood of genetic sequence data, from diverse sources, requiring rapid computational analysis. Rapid analysis can be achieved by sampling a subset of positions in each sequence. Previous sequence-sampling methods, such as minimizers, syncmers and minimally overlapping words, were developed by heuristic intuition, and are not optimal. We present a sequence-sampling approach that provably optimizes sensitivity for a whole class of sequence comparison methods, for randomly evolving sequences. It is likely near-optimal for a wide range of alignment-based and alignment-free analyses. For real biological DNA, it increases specificity by avoiding simple repeats. Our approach generalizes universal hitting sets (which guarantee to sample a sequence at least once) and polar sets (which guarantee to sample a sequence at most once). This helps us understand how to do rapid sequence analysis as accurately as possible. Source code is freely available at https://gitlab.com/mcfrith/noverlap. Supplementary data are available at Bioinformatics online.

Strobealign: flexible seed size enables ultra-fast and accurate read alignment

Read alignment is often the computational bottleneck in analyses. Recently, several advances have been made on seeding methods for fast sequence comparison. We combine two such methods, syncmers and strobemers, in a novel seeding approach for constructing dynamic-sized fuzzy seeds and implement the method in a short-read aligner, strobealign. The seeding is fast to construct and effectively reduces repetitiveness in the seeding step, as shown using a novel metric E-hits. strobealign is several times faster than traditional aligners at similar and sometimes higher accuracy while being both faster and more accurate than more recently proposed aligners for short reads of lengths 150nt and longer. Availability: https://github.com/ksahlin/strobealign

KINN: An alignment-free accurate phylogeny reconstruction method based on inner distance distributions of k-mer pairs in biological sequences

Alignment-based methods have faced disadvantages in sequence comparison and phylogeny reconstruction due to their high computational complexity. Alignment-free methods for sequence comparison and phylogeny inference have attracted a great deal of attention in recent years. Here, we explore an alignment-free approach that uses inner distance distributions of k-mer pairs in biological sequences for phylogeny inference. For every sequence in a dataset, our method transforms the sequence into a numeric feature vector consisting of features each representing a specific k-mer pair’s contribution to the characterization of the sequentiality uniqueness of the sequence. This newly defined k-mer pair’s contribution is an integration of the reverse Kullback-Leibler divergence, pseudo mode and the classic entropy of an inner distance distribution of the k-mer pair in the sequence. Our method has been tested on datasets of complete genome sequences, complete protein sequences, and gene sequences of rRNA of various lengths. Our method achieves the best performance in comparison with state-of-the-art alignment-free methods as measured by the Robinson-Foulds distance between the reference and the constructed phylogeny trees.

Insights from analyses of low complexity regions with canonical methods for protein sequence comparison.

Low complexity regions are fragments of protein sequences composed of only a few types of amino acids. These regions frequently occur in proteins and can play an important role in their functions. However, scientists are mainly focused on regions characterized by high diversity of amino acid composition. Similarity between regions of protein sequences frequently reflect functional similarity between them. In this article, we discuss strengths and weaknesses of the similarity analysis of low complexity regions using BLAST, HHblits and CD-HIT. These methods are considered to be the gold standard in protein similarity analysis and were designed for comparison of high complexity regions. However, we lack specialized methods that could be used to compare the similarity of low complexity regions. Therefore, we investigated the existing methods in order to understand how they can be applied to compare such regions. Our results are supported by exploratory study, discussion of amino acid composition and biological roles of selected examples. We show that existing methods need improvements to efficiently search for similar low complexity regions. We suggest features that have to be re-designed specifically for comparing low complexity regions: scoring matrix, multiple sequence alignment, e-value, local alignment and clustering based on a set of representative sequences. Results of this analysis can either be used to improve existing methods or to create new methods for the similarity analysis of low complexity regions.

Reliability and Utility of Standard Gene Sequence Barcodes for the Identification and Differentiation of Cyst Nematodes of the Genus Heterodera

Difficulties inherent in the morphological identification of cyst nematodes of the genus Heterodera Schmidt, 1871, an important lineage of plant parasites, has led to broad adoption of molecular methods for diagnosing and differentiating species. The pool of publicly available sequence data has grown significantly over the past few decades, and over half of all known species of Heterodera have been characterized using one or more molecular markers commonly employed in DNA barcoding (18S, internal transcribed spacer [ITS], 28S, coxI). But how reliable are these data and how useful are these four markers for differentiating species? We downloaded all 18S, ITS, 28S, and coxI gene sequences available on the National Center for Biotechnology Information (NCBI) database, GenBank, for all species of Heterodera for which data were available. Using a combination of sequence comparison and tree-based phylogenetic methods, we evaluated this dataset for erroneous or otherwise problematic sequences and examined the utility of each molecular marker for the delineation of species. Although we find the rate of obviously erroneous sequences to be low, all four molecular markers failed to differentiate between at least one species pair. Our results suggest that while a combination of multiple markers is best for species identification, the coxI marker shows the most utility for species differentiation and should be favored over 18S, ITS, and 28S, where resources are limited. Presently, less than half the valid species of Heterodera have a sequence of coxI available, and only a third have more than one sequence of this marker.

Phylogenies from unaligned proteomes using sequence environments of amino acid residues.

Alignment-free methods for sequence comparison and phylogeny inference have attracted a great deal of attention in recent years. Several algorithms have been implemented in diverse software packages. Despite the great number of existing methods, most of them are based on word statistics. Although they propose different filtering and weighting strategies and explore different metrics, their performance may be limited by the phylogenetic signal preserved in these words. Herein, we present a different approach based on the species-specific amino acid neighborhood preferences. These differential preferences can be assessed in the context of vector spaces. In this way, a distance-based method to build phylogenies has been developed and implemented into an easy-to-use R package. Tests run on real-world datasets show that this method can reconstruct phylogenetic relationships with high accuracy, and often outperforms other alignment-free approaches. Furthermore, we present evidence that the new method can perform reliably on datasets formed by non-orthologous protein sequences, that is, the method not only does not require the identification of orthologous proteins, but also does not require their presence in the analyzed dataset. These results suggest that the neighborhood preference of amino acids conveys a phylogenetic signal that may be of great utility in phylogenomics.

Contrastive learning on protein embeddings enlightens midnight zone.

Experimental structures are leveraged through multiple sequence alignments, or more generally through homology-based inference (HBI), facilitating the transfer of information from a protein with known annotation to a query without any annotation. A recent alternative expands the concept of HBI from sequence-distance lookup to embedding-based annotation transfer (EAT). These embeddings are derived from protein Language Models (pLMs). Here, we introduce using single protein representations from pLMs for contrastive learning. This learning procedure creates a new set of embeddings that optimizes constraints captured by hierarchical classifications of protein 3D structures defined by the CATH resource. The approach, dubbed ProtTucker, has an improved ability to recognize distant homologous relationships than more traditional techniques such as threading or fold recognition. Thus, these embeddings have allowed sequence comparison to step into the ‘midnight zone’ of protein similarity, i.e. the region in which distantly related sequences have a seemingly random pairwise sequence similarity. The novelty of this work is in the particular combination of tools and sampling techniques that ascertained good performance comparable or better to existing state-of-the-art sequence comparison methods. Additionally, since this method does not need to generate alignments it is also orders of magnitudes faster. The code is available at https://github.com/Rostlab/EAT.

Numerical Characterization of DNA Sequences for Alignment-free Sequence Comparison - A Review.

Biological macromolecules, namely, DNA, RNA, and protein, have their building blocks organized in a particular sequence and the sequential arrangement encodes the evolutionary history of the organism (species). Hence, biological sequences have been used for studying evolutionary relationships among the species. This is usually carried out by Multiple Sequence Algorithms (MSA). Due to certain limitations of MSA, alignment-free sequence comparison methods were developed. The present review is on alignment-free sequence comparison methods carried out using the numerical characterization of DNA sequences. The graphical representation of DNA sequences by chaos game representation and other 2-dimensional and 3-dimensional methods are discussed. The evolution of numerical characterization from the various graphical representations and the application of the DNA invariants thus computed in phylogenetic analysis are presented. The extension of computing molecular descriptors in chemometrics to the calculation of a new set of DNA invariants and their use in alignment-free sequence comparison in an N-dimensional space and construction of phylogenetic trees are also reviewed. The phylogenetic tress constructed by the alignment-free sequence comparison methods using DNA invariants were found to be better than those constructed using alignment-based tools such as PHLYIP and ClustalW. One of the graphical representation methods is now extended to study viral sequences of infectious diseases for the identification of conserved regions to design peptidebased vaccines by combining numerical characterization and graphical representation.

A new feature vector model for alignment-free DNA sequence similarity analysis

Improvements in technology have triggered the production of big data. Within this scope, enormous amounts of biological data have been generated. A number of analysis methods have been developed to access the information contained in biological data. DNA sequence analysis has drawn particular attention in recent years. As an alternative to alignment-based sequence comparison methods that have high computational costs, alignment-free comparison methods have emerged. These m ethods c an c alculate sequence s imilarity by applying different dimensions of numerical characterizations. In this paper, we propose a novel alignment-free DNA sequence analysis method based on a feature extraction strategy. The method utilizes numerical characterization and is implemented by calculating mean distance of the transitions, mean distance of the nucleotide duplications, and the base frequencies. The method then measures the similarity between 7-dimensional vectors that are obtained through feature extraction. Using this approach, we conducted a sequence similarity analysis of two different DNA sequence datasets of different lengths to demonstrate the effectiveness of the method. The proposed method shows that a simple and successful feature vector can be obtained when DNA sequences having many properties are used in combination with appropriate and effective descriptors. With this strategy, reasonable results were obtained with a low computational cost.

Gram-negative outer-membrane proteins with multiple β-barrel domains

Outer-membrane beta barrels (OMBBs) are found in the outer membrane of gram-negative bacteria and eukaryotic organelles. OMBBs fold as antiparallel β-sheets that close onto themselves, forming pores that traverse the membrane. Currently known structures include only one barrel, of 8 to 36 strands, per chain. The lack of multi-OMBB chains is surprising, as most OMBBs form oligomers, and some function only in this state. Using a combination of sensitive sequence comparison methods and coevolutionary analysis tools, we identify many proteins combining multiple beta barrels within a single chain; combinations that include eight-stranded barrels prevail. These multibarrels seem to be the result of independent, lineage-specific fusion and amplification events. The absence of multibarrels that are universally conserved in bacteria with an outer membrane, coupled with their frequent de novo genesis, suggests that their functions are not essential but rather beneficial in specific environments. Adjacent barrels of complementary function within the same chain may allow for functions beyond those of the individual barrels.

Characterization of the Highly Efficient Acid-Stable Xylanase and β-Xylosidase System from the Fungus Byssochlamys spectabilis ATHUM 8891 (Paecilomyces variotii ATHUM 8891).

Two novel xylanolytic enzymes, a xylanase and a β-xylosidase, were simultaneously isolated and characterized from the extracellular medium of Byssochlamys spectabilis ATHUM 8891 (anamorph Paecilomyces variotii ATHUM 8891), grown on Brewer’s Spent Grain as a sole carbon source. They represent the first pair of characterized xylanolytic enzymes of the genus Byssochlamys and the first extensively characterized xylanolytic enzymes of the family Thermoascaceae. In contrast to other xylanolytic enzymes isolated from the same family, both enzymes are characterized by exceptional thermostability and stability at low pH values, in addition to activity optima at temperatures around 65 °C and acidic pH values. Applying nano-LC-ESI-MS/MS analysis of the purified SDS-PAGE bands, we sequenced fragments of both proteins. Based on sequence-comparison methods, both proteins appeared conserved within the genus Byssochlamys. Xylanase was classified within Glycoside Hydrolase family 11 (GH 11), while β-xylosidase in Glycoside Hydrolase family 3 (GH 3). The two enzymes showed a synergistic action against xylan by rapidly transforming almost 40% of birchwood xylan to xylose. The biochemical profile of both enzymes renders them an efficient set of biocatalysts for the hydrolysis of xylan in demanding biorefinery applications.

Alignment free sequence comparison methods and reservoir host prediction.

MotivationThe emergence and subsequent pandemic of the SARS-CoV-2 virus raised urgent questions about its origin and, particularly, its reservoir host. These types of questions are long-standing problems in the management of emerging infectious diseases and are linked to virus discovery programs and the prediction of viruses that are likely to become zoonotic. Conventional means to identify reservoir hosts have relied on surveillance, experimental studies and phylogenetics. More recently, machine learning approaches have been applied to generate tools to swiftly predict reservoir hosts from sequence data.ResultsHere, we extend a recent work that combined sequence alignment and a mixture of alignment-free approaches using a gradient boosting machines (GBMs) machine learning model, which integrates genomic traits (GT) and phylogenetic neighbourhood (PN) signatures to predict reservoir hosts. We add a more uniform approach by applying Machine Learning with Digital Signal Processing (MLDSP)-based structural patterns (M-SP). The extended model was applied to an existing virus/reservoir host dataset and to the SARS-CoV-2 and related viruses and generated an improvement in prediction accuracy.Availability and implementationThe source code used in this work is freely available at https://github.com/bill1167/hostgbms.Supplementary informationSupplementary data are available at Bioinformatics online.

Protein Sequence Analysis Using the MPI Bioinformatics Toolkit.

The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) provides interactive access to a wide range of the best-performing bioinformatics tools and databases, including the state-of-the-art protein sequence comparison methods HHblits and HHpred. The Toolkit currently includes 35 external and in-house tools, covering functionalities such as sequence similarity searching, prediction of sequence features, and sequence classification. Due to this breadth of functionality, the tight interconnection of its constituent tools, and its ease of use, the Toolkit has become an important resource for biomedical research and for teaching protein sequence analysis to students in the life sciences. In this article, we provide detailed information on utilizing the three most widely accessed tools within the Toolkit: HHpred for the detection of homologs, HHpred in conjunction with MODELLER for structure prediction and homology modeling, and CLANS for the visualization of relationships in large sequence datasets. © 2020 The Authors. Basic Protocol 1: Sequence similarity searching using HHpred Alternate Protocol: Pairwise sequence comparison using HHpred Support Protocol: Building a custom multiple sequence alignment using PSI-BLAST and forwarding it as input to HHpred Basic Protocol 2: Calculation of homology models using HHpred and MODELLER Basic Protocol 3: Cluster analysis using CLANS.

An alignment-free heuristic for fast sequence comparisons with applications to phylogeny reconstruction

BackgroundAlignment-free methods for sequence comparisons have become popular in many bioinformatics applications, specifically in the estimation of sequence similarity measures to construct phylogenetic trees. Recently, the average common substring measure, ACS, and its k-mismatch counterpart, ACSk, have been shown to produce results as effective as multiple-sequence alignment based methods for reconstruction of phylogeny trees. Since computing ACSk takes O(n logkn) time and hence impractical for large datasets, multiple heuristics that can approximate ACSk have been introduced.ResultsIn this paper, we present a novel linear-time heuristic to approximate ACSk, which is faster than computing the exact ACSk while being closer to the exact ACSk values compared to previously published linear-time greedy heuristics. Using four real datasets, containing both DNA and protein sequences, we evaluate our algorithm in terms of accuracy, runtime and demonstrate its applicability for phylogeny reconstruction. Our algorithm provides better accuracy than previously published heuristic methods, while being comparable in its applications to phylogeny reconstruction.ConclusionsOur method produces a better approximation for ACSk and is applicable for the alignment-free comparison of biological sequences at highly competitive speed. The algorithm is implemented in Rust programming language and the source code is available at https://github.com/srirampc/adyar-rs.

Novel Protein Sequence Comparison Method Based on Transition Probability Graph and Information Entropy.

Aim and Objective: Sequence analysis is one of the foundations in bioinformatics. It is widely used to find out the feature metrics hidden in the sequence. Otherwise, the graphical representation of the biologic sequence is an important tool for sequencing analysis. This study is undertaken to find out a new graphical representation of biosequences. The transition probability is used to describe amino acid combinations of protein sequences. The combinations are composed of amino acids directly adjacent to each other or separated by multiple amino acids. The transition probability graph is built up by the transition probabilities of amino acid combinations. Next, a map is defined as a representation from the transition probability graph to transition probability vector by the k-order transition probability graph. Transition entropy vectors are developed by the transition probability vector and information entropy. Finally, the proposed method is applied to two separate applications, 499 HA genes of H1N1, and 95 coronaviruses. By constructing a phylogenetic tree, it was found that the results of each application are consistent with other studies. The graphical representation proposed in this article is a practical and correct method.

A new graph-theoretic approach to determine the similarity of genome sequences based on nucleotide triplets

Methods of finding sequence similarity play a significant role in computational biology. Owing to the rapid increase of genome sequences in public databases, the evolutionary relationship of species becomes more challenging. But traditional alignment-based methods are found inappropriate due to their time-consuming nature. Therefore, it is necessary to find a faster method, which applies to species phylogeny. In this paper, a new graph-theory based alignment-free sequence comparison method is proposed. A complete-bipartite graph is used to represent each genome sequence based on its nucleotide triplets. Subsequently, with the help of the weights of edges of the graph, a vector descriptor is formed. Finally, the phylogenetic tree is drawn using the UPGMA algorithm. In the present case, the datasets for comparison are related to mammals, viruses, and bacteria. In most of the cases, the phylogeny in the present case is found to be more satisfactory as compared to earlier methods.

A novel alignment-free DNA sequence similarity analysis approach based on top-k n-gram match-up

DNA sequence similarity analysis is an essential task in computational biology and bioinformatics. In nearly all research that explores evolutionary relationships, gene function analysis, protein structure prediction and sequence retrieving, it is necessary to perform similarity calculations. As an alternative to alignment-based sequence comparison methods, which result in high computational cost, alignment-free methods have emerged that calculate similarity by digitizing the sequence in a different space. In this paper, we proposed an alignment-free DNA sequence similarity analysis method based on top-k n-gram matches, with the prediction that common repeating DNA subsections indicate high similarity between DNA sequences. In our method, we determined DNA sequence similarities by measuring similarity among feature vectors created according to top-k n-gram match-up scores without the use of similarity functions. We applied the similarity calculation for three different DNA data sets of different lengths. The phylogenetic relationships revealed by our method show that our trees coincide almost completely with the results of the MEGA software, which is based on sequence alignment. Our findings show that a certain number of frequently recurring common sequence patterns have the power to characterize DNA sequences.