Exact global alignment using A* with chaining seed heuristic and match pruning

MOTIVATION

Sequence alignment has been at the core of computational biology for half a century. Still, it is an open problem to design a practical algorithm for exact alignment of a pair of related sequences in linear-like time.

RESULTS

We solve exact global pairwise alignment with respect to edit distance by using the A* shortest path algorithm. In order to efficiently align long sequences with high divergence, we extend the recently proposed seed heuristic with match chaining, gap costs, and inexact matches. We additionally integrate the novel match pruning technique and diagonal transition to improve the A* search. We prove the correctness of our algorithm, implement it in the A*PA aligner, and justify our extensions intuitively and empirically.

On random sequences of divergence d=4% and length n, the empirical runtime of A*PA scales near-linearly with length (best fit n1.06, n≤107 bp). A similar scaling remains up to d=12% (best fit n1.24, n≤107 bp). For n=107 bp and d=4%, A*PA reaches >500× speedup compared to the leading exact aligners Edlib and BiWFA. The performance of A*PA is highly influenced by long gaps. On long (n>500kb) ONT reads of a human sample it efficiently aligns sequences with d<10%, leading to 3× median speedup compared to Edlib and BiWFA. When the sequences come from different human samples, A*PA performs 1.7× faster than Edlib and BiWFA.

AVAILABILITY AND IMPLEMENTATION

github.com/RagnarGrootKoerkamp/astar-pairwise-aligner.

Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective

The encounter of large amounts of biological sequence data generated during the last decades and the algorithmic and hardware improvements have offered the possibility to apply machine learning techniques in bioinformatics. While the machine learning community is aware of the necessity to rigorously distinguish data transformation from data comparison and adopt reasonable combinations thereof, this awareness is often lacking in the field of comparative sequence analysis. With realization of the disadvantages of alignments for sequence comparison, some typical applications use more and more so-called alignment-free approaches. In light of this development, we present a conceptual framework for alignment-free sequence comparison, which highlights the delineation of: 1) the sequence data transformation comprising of adequate mathematical sequence coding and feature generation, from 2) the subsequent (dis-)similarity evaluation of the transformed data by means of problem-specific but mathematically consistent proximity measures. We consider coding to be an information-loss free data transformation in order to get an appropriate representation, whereas feature generation is inevitably information-lossy with the intention to extract just the task-relevant information. This distinction sheds light on the plethora of methods available and assists in identifying suitable methods in machine learning and data analysis to compare the sequences under these premises.

The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers

In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy. In combination with interpretable machine learning classifier models based on generalized learning vector quantization, a powerful methodology for sequence classification is achieved which allows substantial knowledge extraction in addition to the high classification ability due to the model-inherent robustness. Any potential (slightly) inferior performance of the used classifier is compensated by the additional knowledge provided by interpretable models. This knowledge may assist the user in the analysis and understanding of the used data and considered task. After theoretical justification of the concepts, we demonstrate the approach for various example data sets covering different areas in biomolecular sequence analysis.

A Review of Parallel Implementations for the Smith-Waterman Algorithm

Abstract

The rapid advances in sequencing technology have led to an explosion of sequence data. Sequence alignment is the central and fundamental problem in many sequence analysis procedure, while local alignment is often the kernel of these algorithms. Usually, Smith–Waterman algorithm is used to find the best subsequence match between given sequences. However, the high time complexity makes the algorithm time-consuming. A lot of approaches have been developed to accelerate and parallelize it, such as vector-level parallelization, thread-level parallelization, process-level parallelization, and heterogeneous acceleration, but the current researches seem unsystematic, which hinders the further research of parallelizing the algorithm. In this paper, we summarize the current research status of parallel local alignments and describe the data layout in these work. Based on the research status, we emphasize large-scale genomic comparisons. By surveying some typical alignment tools’ performance, we discuss some possible directions in the future. We hope our work will provide the developers of the alignment tool with technical principle support, and help researchers choose proper alignment tools.

Graphic abstract

A tri-tuple coordinate system derived for fast and accurate analysis of the colored de Bruijn graph-based pangenomes

Background

With the rapid development of accurate sequencing and assembly technologies, an increasing number of high-quality chromosome-level and haplotype-resolved assemblies of genomic sequences have been derived, from which there will be great opportunities for computational pangenomics. Although genome graphs are among the most useful models for pangenome representation, their structural complexity makes it difficult to present genome information intuitively, such as the linear reference genome. Thus, efficiently and accurately analyzing the genome graph spatial structure and coordinating the information remains a substantial challenge.

Results

We developed a new method, a colored superbubble (cSupB), that can overcome the complexity of graphs and organize a set of species- or population-specific haplotype sequences of interest. Based on this model, we propose a tri-tuple coordinate system that combines an offset value, topological structure and sample information. Additionally, cSupB provides a novel method that utilizes complete topological information and efficiently detects small indels (< 50 bp) for highly similar samples, which can be validated by simulated datasets. Moreover, we demonstrated that cSupB can adapt to the complex cycle structure.

Conclusions

Although the solution is made suitable for increasingly complex genome graphs by relaxing the constraint, the directed acyclic graph, the motif cSupB and the cSupB method can be extended to any colored directed acyclic graph. We anticipate that our method will facilitate the analysis of individual haplotype variants and population genomic diversity. We have developed a C +  + program for implementing our method that is available at https://github.com/eggleader/cSupB.

Supplementary information

The online version contains supplementary material available at 10.1186/s12859-021-04149-w.

Sequence Comparison Without Alignment: The SpaM Approaches

Sequence alignment is at the heart of DNA and protein sequence analysis. For the data volumes that are nowadays produced by massively parallel sequencing technologies, however, pairwise and multiple alignment methods are often too slow. Therefore, fast alignment-free approaches to sequence comparison have become popular in recent years. Most of these approaches are based on word frequencies, for words of a fixed length, or on word-matching statistics. Other approaches are using the length of maximal word matches. While these methods are very fast, most of them rely on ad hoc measures of sequences similarity or dissimilarity that are hard to interpret. In this chapter, I describe a number of alignment-free methods that we developed in recent years. Our approaches are based on spaced-word matches ("SpaM"), i.e. on inexact word matches, that are allowed to contain mismatches at certain pre-defined positions. Unlike most previous alignment-free approaches, our approaches are able to accurately estimate phylogenetic distances between DNA or protein sequences using a stochastic model of molecular evolution.

Algorithms meet sequencing technologies - 10th edition of the RECOMB-Seq workshop

DNA and RNA sequencing is a core technology in biological and medical research. The high throughput of these technologies and the consistent development of new experimental assays and biotechnologies demand the continuous development of methods to analyze the resulting data. The RECOMB Satellite Workshop on Massively Parallel Sequencing brings together leading researchers in computational genomics to discuss emerging frontiers in algorithm development for massively parallel sequencing data. The 10th meeting in this series, RECOMB-Seq 2020, was scheduled to be held in Padua, Italy, but due to the ongoing COVID-19 pandemic, the meeting was carried out virtually instead. The online workshop featured keynote talks by Paola Bonizzoni and Zamin Iqbal, two highlight talks, ten regular talks, and three short talks. Seven of the works presented in the workshop are featured in this edition of iScience, and many of the talks are available online in the RECOMB-Seq 2020 YouTube channel.

Improved Analysis of High-Throughput Sequencing Data Using Small Universal k-Mer Hitting Sets

High-throughput sequencing machines can read millions of DNA molecules in parallel in a short time and at a relatively low cost. As a consequence, researchers have access to databases with millions of genomic samples. Searching and analyzing these large amounts of data require efficient algorithms.Universal hitting sets are sets of words that must be present in any long enough string. Using small universal hitting sets, it is possible to increase the efficiency of many high-throughput sequencing data analyses. But, generating minimum-size universal hitting sets is a hard problem. In this chapter, we cover our algorithmic developments to produce compact universal hitting sets and some of their potential applications.

Pyfastx: a robust Python package for fast random access to sequences from plain and gzipped FASTA/Q files

FASTA and FASTQ are the most widely used biological data formats that have become the de facto standard to exchange sequence data between bioinformatics tools. With the avalanche of next-generation sequencing data, the amount of sequence data being deposited and accessed in FASTA/Q formats is increasing dramatically. However, the existing tools have very low efficiency at random retrieval of subsequences due to the requirement of loading the entire index into memory. In addition, most existing tools have no capability to build index for large FASTA/Q files because of the limited memory. Furthermore, the tools do not provide support to randomly accessing sequences from FASTA/Q files compressed by gzip, which is extensively adopted by most public databases to compress data for saving storage. In this study, we developed pyfastx as a versatile Python package with commonly used command-line tools to overcome the above limitations. Compared to other tools, pyfastx yielded the highest performance in terms of building index and random access to sequences, particularly when dealing with large FASTA/Q files with hundreds of millions of sequences. A key advantage of pyfastx over other tools is that it offers an efficient way to randomly extract subsequences directly from gzip compressed FASTA/Q files without needing to uncompress beforehand. Pyfastx can easily be installed from PyPI (https://pypi.org/project/pyfastx) and the source code is freely available at https://github.com/lmdu/pyfastx.

A Randomized Parallel Algorithm for Efficiently Finding Near-Optimal Universal Hitting Sets

As the volume of next generation sequencing data increases, an urgent need for algorithms to efficiently process the data arises. Universal hitting sets (UHS) were recently introduced as an alternative to the central idea of minimizers in sequence analysis with the hopes that they could more efficiently address common tasks such as computing hash functions for read overlap, sparse suffix arrays, and Bloom filters. A UHS is a set of k -mers that hit every sequence of length L , and can thus serve as indices to L -long sequences. Unfortunately, methods for computing small UHSs are not yet practical for real-world sequencing instances due to their serial and deterministic nature, which leads to long runtimes and high memory demands when handling typical values of k (e.g. k > 13 ). To address this bottleneck, we present two algorithmic innovations to significantly decrease runtime while keeping memory usage low: (i) we leverage advanced theoretical and architectural techniques to parallelize and decrease memory usage in calculating k -mer hitting numbers; and (ii) we build upon techniques from randomized Set Cover to select universal k -mers much faster. We implemented these innovations in PASHA, the first randomized parallel algorithm for generating nearoptimal UHSs, which newly handles k > 13 . We demonstrate empirically that PASHA produces sets only slightly larger than those of serial deterministic algorithms; moreover, the set size is provably guaranteed to be within a small constant factor of the optimal size. PASHA's runtime and memory-usage improvements are orders of magnitude faster than the current best algorithms. We expect our newly-practical construction of UHSs to be adopted in many high-throughput sequence analysis pipelines.

'Multi-SpaM': a maximum-likelihood approach to phylogeny reconstruction using multiple spaced-word matches and quartet trees

Word-based or 'alignment-free' methods for phylogeny inference have become popular in recent years. These methods are much faster than traditional, alignment-based approaches, but they are generally less accurate. Most alignment-free methods calculate 'pairwise' distances between nucleic-acid or protein sequences; these distance values can then be used as input for tree-reconstruction programs such as neighbor-joining. In this paper, we propose the first word-based phylogeny approach that is based on 'multiple' sequence comparison and 'maximum likelihood'. Our algorithm first samples small, gap-free alignments involving four taxa each. For each of these alignments, it then calculates a quartet tree and, finally, the program 'Quartet MaxCut' is used to infer a super tree for the full set of input taxa from the calculated quartet trees. Experimental results show that trees produced with our approach are of high quality.

The number of k-mer matches between two DNA sequences as a function of k and applications to estimate phylogenetic distances

We study the number Nk of length-k word matches between pairs of evolutionarily related DNA sequences, as a function of k. We show that the Jukes-Cantor distance between two genome sequences-i.e. the number of substitutions per site that occurred since they evolved from their last common ancestor-can be estimated from the slope of a function F that depends on Nk and that is affine-linear within a certain range of k. Integers kmin and kmax can be calculated depending on the length of the input sequences, such that the slope of F in the relevant range can be estimated from the values F(kmin) and F(kmax). This approach can be generalized to so-called Spaced-word Matches (SpaM), where mismatches are allowed at positions specified by a user-defined binary pattern. Based on these theoretical results, we implemented a prototype software program for alignment-free sequence comparison called Slope-SpaM. Test runs on real and simulated sequence data show that Slope-SpaM can accurately estimate phylogenetic distances for distances up to around 0.5 substitutions per position. The statistical stability of our results is improved if spaced words are used instead of contiguous words. Unlike previous alignment-free methods that are based on the number of (spaced) word matches, Slope-SpaM produces accurate results, even if sequences share only local homologies.

Read-SpaM: assembly-free and alignment-free comparison of bacterial genomes with low sequencing coverage

BACKGROUND

In many fields of biomedical research, it is important to estimate phylogenetic distances between taxa based on low-coverage sequencing reads. Major applications are, for example, phylogeny reconstruction, species identification from small sequencing samples, or bacterial strain typing in medical diagnostics.

RESULTS

We adapted our previously developed software program Filtered Spaced-Word Matches (FSWM) for alignment-free phylogeny reconstruction to take unassembled reads as input; we call this implementation Read-SpaM.

CONCLUSIONS

Test runs on simulated reads from semi-artificial and real-world bacterial genomes show that our approach can estimate phylogenetic distances with high accuracy, even for large evolutionary distances and for very low sequencing coverage.