Patterns In Biological Sequences Research Articles

The potential and pitfalls of large language models in molecular biosciences

Large language models (LLMs) have revolutionized information processing and are now being applied to complex problems in molecular biosciences. LLMs excel at learning patterns in biological sequences, enabling applications such as predicting the function of DNA regulatory elements, analysing chromatin modifications and predicting the effects of genetic variants. In drug discovery, LLMs are being used to optimize drug properties, design novel molecules and even automate compound synthesis. However, realizing the full potential of LLMs in the molecular biosciences requires addressing challenges such as designing effective learning objectives, generating high-quality, domain-specific datasets and building trust in AI-assisted decision-making. As these challenges are addressed, LLMs hold immense potential for advancing research and driving ground-breaking discoveries in molecular biosciences.

Trie-PMS8: A trie-tree based robust solution for planted motif search problem

Finding patterns in biological sequences is a crucial and intriguing task. This paper explores the (Ɩ, d) motif search problem, also known as Planted Motif Search (PMS), and discusses its challenging nature as an NP-hard problem. PMS and (Ɩ, d) motif search algorithms are believed to represent the next generation of tools for motif discovery. In this context, PMS deals with n biological sequences and two parameters, Ɩ and d, to identify sequences of Ɩ length that occur in all input strings with, at most, d mismatches. Many existing exact PMS algorithms exhibit exponential time complexity in worst-case scenarios. This paper introduces an innovative algorithm that focuses on improving the efficiency of the sample-driven portion of the process. Specifically, dynamic programming techniques are employed to avoid redundant calculations in frequently used subtrees. Furthermore, this paper presents novel approaches to enhance algorithm performance, such as utilizing a trie tree that significantly reduces the time for the “sort rows by size” step. It has also reduced the spaces that take linked lists on LL-PMS8 (Hasan et al., Jun., 2022) or reduced the number of l-mers. Using trie tree as the main way to speed things up gives a much better result than older versions of PMS methods like LL-PMS8 (Hasan et al., Jun., 2022). Overall time complexity reduced than the previous method is 26.17 % and 16.48 % for real-world and generated datasets (Hasan et al., 2020).

Compression-Complexity Measures for Analysis and Classification of Coronaviruses

Finding a vaccine or specific antiviral treatment for a global pandemic of virus diseases (such as the ongoing COVID-19) requires rapid analysis, annotation and evaluation of metagenomic libraries to enable a quick and efficient screening of nucleotide sequences. Traditional sequence alignment methods are not suitable and there is a need for fast alignment-free techniques for sequence analysis. Information theory and data compression algorithms provide a rich set of mathematical and computational tools to capture essential patterns in biological sequences. In this study, we investigate the use of compression-complexity (Effort-to-Compress or ETC and Lempel-Ziv or LZ complexity) based distance measures for analyzing genomic sequences. The proposed distance measure is used to successfully reproduce the phylogenetic trees for a mammalian dataset consisting of eight species clusters, a set of coronaviruses belonging to group I, group II, group III, and SARS-CoV-1 coronaviruses, and a set of coronaviruses causing COVID-19 (SARS-CoV-2), and those not causing COVID-19. Having demonstrated the usefulness of these compression complexity measures, we employ them for the automatic classification of COVID-19-causing genome sequences using machine learning techniques. Two flavors of SVM (linear and quadratic) along with linear discriminant and fine K Nearest Neighbors classifer are used for classification. Using a data set comprising 1001 coronavirus sequences (causing COVID-19 and those not causing COVID-19), a classification accuracy of 98% is achieved with a sensitivity of 95% and a specificity of 99.8%. This work could be extended further to enable medical practitioners to automatically identify and characterize coronavirus strains and their rapidly growing mutants in a fast and efficient fashion.

Neural network architecture search with AMBER

Deep learning applied to genomics can learn patterns in biological sequences, but designing such models requires expertise and effort. Recent work demonstrates the efficiency of a neural network architecture search algorithm in optimizing genomic models.

EMS3: An Improved Algorithm for Finding Edit-Distance Based Motifs.

Discovering patterns in biological sequences is a crucial step to extract useful information from them. Motifs can be viewed as patterns that occur exactly or with minor changes across some or all of the biological sequences. Motif search has numerous applications including the identification of transcription factors and their binding sites, composite regulatory patterns, similarity among families of proteins, etc. The general problem of motif search is intractable. One of the most studied models of motif search proposed in literature is Edit-distance based Motif Search (EMS). In EMS, the goal is to find all the patterns of length l that occur with an edit-distance of at most d in each of the input sequences. EMS algorithms existing in the literature do not scale well on challenging instances and large datasets. In this paper, the current state-of-the-art EMS solver is advanced by exploiting the idea of dimension reduction. A novel idea to reduce the cardinality of the alphabet is proposed. The algorithm we propose, EMS3, is an exact algorithm. I.e., it finds all the motifs present in the input sequences. EMS3 can be also viewed as a divide and conquer algorithm. In this paper, we provide theoretical analyses to establish the efficiency of EMS3. Extensive experiments on standard benchmark datasets (synthetic and real-world) show that the proposed algorithm outperforms the existing state-of-the-art algorithm (EMS2).

Asymmetron: a toolkit for the identification of strand asymmetry patterns in biological sequences.

DNA strand asymmetries can have a major effect on several biological functions, including replication, transcription and transcription factor binding. As such, DNA strand asymmetries and mutational strand bias can provide information about biological function. However, a versatile tool to explore this does not exist. Here, we present Asymmetron, a user-friendly computational tool that performs statistical analysis and visualizations for the evaluation of strand asymmetries. Asymmetron takes as input DNA features provided with strand annotation and outputs strand asymmetries for consecutive occurrences of a single DNA feature or between pairs of features. We illustrate the use of Asymmetron by identifying transcriptional and replicative strand asymmetries of germline structural variant breakpoints. We also show that the orientation of the binding sites of 45% of human transcription factors analyzed have a significant DNA strand bias in transcribed regions, that is also corroborated in ChIP-seq analyses, and is likely associated with transcription. In summary, we provide a novel tool to assess DNA strand asymmetries and show how it can be used to derive new insights across a variety of biological disciplines.

MFEA: An evolutionary approach for motif finding in DNA sequences

Identification of short repeating patterns in biological sequences, mostly known as motif, is important for understanding the genetic regulatory system of a living being. But weak conservation of motifs makes it an NP-hard problem and poses a challenge in computational biology. In this work, we have modeled the motif search problem from meta-heuristic perspective. We have proposed and evaluated an evolutionary approach, in which, we will search candidate motifs with a heuristic so that we can find the real motifs of the data set without exploring rigorously. Our method minimizes the trade between exploration and exploitation of the search space with a defined mutation technique using normal distribution and finds an efficient way to measure the fitness of a candidate motif to be real motif. We have used benchmark data set to evaluate the fitness of found motifs for each species, and our approach gives accurate motifs for each of them.

Discovering of gapped motifs using particle swarm optimisation

In bioinformatics, motif discovery is one of the fundamental and important computational problems. Identifying these recurring patterns in biological sequences helps us to better understand the mechanisms that regulate gene expression. Recently several evolutionary algorithms have been developed to solve motif discovery problem, because of their efficiency in searching multidimensional solution space. HPSO, IPSO-GA, PMbPSO and PSO+ are based on particle swarm optimisation (PSO) algorithms. Among these, PSO+ is the first one to be proposed for finding gapped motifs. PSO+ is less efficient in finding gapped motifs that are located at the centre of a motif. Here, our contribution is, to find gapped motifs that are present at the centre of two conserved regions efficiently by adopting features of PSO to solve the problem. We performed experiments on simulated and real biological datasets. It is observed that our approach is able to detect known gapped TFBS more accurately and efficiently.

Frequent Patterns Algorithm of Biological Sequences based on Pattern Prefix-tree

In the application of bioinformatics, the existing algorithms cannot be directly and efficiently implement sequence pattern mining. Two fast and efficient biological sequence pattern mining algorithms for biological single sequence and multiple sequences are proposed in this paper. The concept of the basic pattern is proposed, and on the basis of mining frequent basic patterns, the frequent pattern is excavated by constructing prefix trees for frequent basic patterns. The proposed algorithms implement rapid mining of frequent patterns of biological sequences based on pattern prefix trees. In experiment the family sequence data in the pfam protein database is used to verify the performance of the proposed algorithm. The prediction results confirm that the proposed algorithms can’t only obtain the mining results with effective biological significance, but also improve the running time efficiency of the biological sequence pattern mining.

Patscanui: an intuitive web interface for searching patterns in DNA and protein data.

Patterns in biological sequences frequently signify interesting features in the underlying molecule. Many tools exist to search for well-known patterns. Less support is available for exploratory analysis, where no well-defined patterns are known yet. PatScanUI (https://patscan.secondarymetabolites.org/) provides a highly interactive web interface to the powerful generic pattern search tool PatScan. The complex PatScan-patterns are created in a drag-and-drop aware interface allowing researchers to do rapid prototyping of the often complicated patterns useful to identifying features of interest.

MpBsmi: A new algorithm for the recognition of continuous biological sequence pattern based on index structure

A significant approach for the discovery of biological regulatory rules of genes, protein and their inheritance relationships is the extraction of meaningful patterns from biological sequence data. The existing algorithms of sequence pattern discovery, like MSPM and FBSB, suffice their low efficiency and accuracy. In order to deal with this issue, this paper presents a new algorithm for biological sequence pattern mining abbreviated MpBsmi based on the data index structure. The MpBsmi algorithm employs a sequence position table abbreviated ST and a sequence database index structure named DB-Index for data storing, mining and pattern expansion. The ST and DB-Index of single items are firstly obtained through scanning sequence database once. Then a new algorithm for fast support counting is developed to mine the table ST to identify the frequent single items. Based on a connection strategy, the frequent patterns are expanded and the expanded table ST is updated by scanning the DB-Index. The fast support counting algorithm is used for obtaining the frequent expansion patterns. Finally, a new pruning technique is developed for extended pattern to avoid the generation of unnecessarily large number of candidate patterns. The experiments results on multiple classical protein sequences from the Pfam database validate the performance of the proposed algorithm including the accuracy, stability and scalability. It is showed that the proposed algorithm has achieved the better space efficiency, stability and scalability comparing with MSPM, FBSB which are the two main algorithms for biological sequence mining.

WITHDRAWN: Biological Sequence Pattern Mining Algorithm Based on Data Index Technology

WITHDRAWN: Biological Sequence Pattern Mining Algorithm Based on Data Index Technology

A Method to Avoid Gapped Sequential Patterns in Biological Sequences: Case Study: HIV and Cancer Sequences

A Method to Avoid Gapped Sequential Patterns in Biological Sequences: Case Study: HIV and Cancer Sequences

Randomised sequential and parallel algorithms for efficient quorum planted motif search

Motifs are crucial patterns in biological sequences that have numerous applications. Motif search is an important step in obtaining meaningful patterns from biological data. However, most of the existing algorithms are deterministic and the role of randomisation in this area is still unexploited. This paper focuses on (l,d)-motif model, which is also known as Planted Motif Search (PMS) and proposes an efficient randomised algorithm, named qPMS10, to solve PMS. We utilise the most efficient PMS solver until now, named qPMS9, as a subroutine. We analyse the time complexity of both algorithms and provide a performance comparison of qPMS10 with qPMS9 on standard benchmark datasets. In addition, we offer a parallel implementation of qPMS10 and run tests using up to four processors. Both theoretical and empirical analyses demonstrate that our randomised algorithm outperforms the existing algorithms for solving PMS.

SeqFeatR for the Discovery of Feature-Sequence Associations.

Specific selection pressures often lead to specifically mutated genomes. The open source software SeqFeatR has been developed to identify associations between mutation patterns in biological sequences and specific selection pressures (“features”). For instance, SeqFeatR has been used to discover in viral protein sequences new T cell epitopes for hosts of given HLA types. SeqFeatR supports frequentist and Bayesian methods for the discovery of statistical sequence-feature associations. Moreover, it offers novel ways to visualize results of the statistical analyses and to relate them to further properties. In this article we demonstrate various functions of SeqFeatR with real data. The most frequently used set of functions is also provided by a web server. SeqFeatR is implemented as R package and freely available from the R archive CRAN (http://cran.r-project.org/web/packages/SeqFeatR/index.html). The package includes a tutorial vignette. The software is distributed under the GNU General Public License (version 3 or later). The web server URL is https://seqfeatr.zmb.uni-due.de.

Glucose-Based Regulation of miR-451/AMPK Signaling Depends on the OCT1 Transcription Factor

In aggressive, rapidly growing solid tumors such as glioblastoma multiforme (GBM), cancer cells face frequent dynamic changes in their microenvironment, including the availability of glucose and other nutrients. These challenges require that tumor cells have the ability to adapt in order to survive periods of nutrient/energy starvation. We have identified a reciprocal negative feedback loop mechanism in which the levels of microRNA-451 (miR-451) are negatively regulated through the phosphorylation and inactivation of its direct transcriptional activator OCT1 by 5' AMP-activated protein kinase (AMPK), which is activated by glucose depletion-induced metabolic stress. Conversely, in a glucose-rich environment, unrestrained expression of miR-451 suppresses AMPK pathway activity. These findings uncover miR-451 as a major effector of glucose-regulated AMPK signaling, allowing tumor cell adaptation to variations in nutrient availability in the tumor microenvironment.

QPMS9: an efficient algorithm for quorum Planted Motif Search.

Discovering patterns in biological sequences is a crucial problem. For example, the identification of patterns in DNA sequences has resulted in the determination of open reading frames, identification of gene promoter elements, intron/exon splicing sites, and SH RNAs, location of RNA degradation signals, identification of alternative splicing sites, etc. In protein sequences, patterns have led to domain identification, location of protease cleavage sites, identification of signal peptides, protein interactions, determination of protein degradation elements, identification of protein trafficking elements, discovery of short functional motifs, etc. In this paper we focus on the identification of an important class of patterns, namely, motifs. We study the (ℓ, d) motif search problem or Planted Motif Search (PMS). PMS receives as input n strings and two integers ℓ and d. It returns all sequences M of length ℓ that occur in each input string, where each occurrence differs from M in at most d positions. Another formulation is quorum PMS (qPMS), where the motif appears in at least q% of the strings. We introduce qPMS9, a parallel exact qPMS algorithm that offers significant runtime improvements on DNA and protein datasets. qPMS9 solves the challenging DNA (ℓ, d)-instances (28, 12) and (30, 13). The source code is available at https://code.google.com/p/qpms9/.

TrieAMD: a scalable and efficient apriori motif discovery approach.

Motif discovery is the problem of finding recurring patterns in biological sequences. It is one of the hardest and long-standing problems in bioinformatics. Apriori is a well-known data-mining algorithm for the discovery of frequent patterns in large datasets. In this paper, we apply the Apriori algorithm and use the Trie data structure to discover motifs. We propose several modifications so that we can adapt the classic Apriori to our problem. Experiments are conducted on Tompa's benchmark to investigate the performance of our proposed algorithm, the Trie-based Apriori Motif Discovery (TrieAMD). Results show that our algorithm outperforms all of the tested tools on real datasets for the average sensitivity measure, which means that our approach is able to discover more motifs. In terms of specificity, the performance of our algorithm is comparable to the other tools. The results also confirm both linear time and linear space scalability of the algorithm.

An improved voting algorithm for planted (l, d) motif search

The planted motif search problem is a classical problem in bioinformatics that seeks to identify meaningful patterns in biological sequences. As an NP-complete problem, current algorithms focus on improving the average time complexity and solving challenging instances within an acceptable time. In this paper, we propose a new exact algorithm CVoting that improves the state-of-the-art Voting algorithm. CVoting uses a new hash technique to reduce the space complexity to O(mn+N(l,d)) and a new pruning technique to reduce the average time complexity to Om2nN(l,d)14+3ll. Experimental results show that CVoting outperforms competing algorithms, including PMS1, RISOTTO, Voting and Pmsprune, in both space and time: up to an order of magnitude faster and using less memory in solving challenging instances. The software of the proposed algorithm is publicly available at http://staff.ustc.edu.cn/xuyun/motif.

DFSP: a Depth-First SPelling algorithm for sequential pattern mining of biological sequences

Scientific progress in recent years has led to the generation of huge amounts of biological data, most of which remains unanalyzed. Mining the data may provide insights into various realms of biology, such as finding co-occurring biosequences, which are essential for biological data mining and analysis. Data mining techniques like sequential pattern mining may reveal implicitly meaningful patterns among the DNA or protein sequences. If biologists hope to unlock the potential of sequential pattern mining in their field, it is necessary to move away from traditional sequential pattern mining algorithms, because they have difficulty handling a small number of items and long sequences in biological data, such as gene and protein sequences. To address the problem, we propose an approach called Depth-First SPelling (DFSP) algorithm for mining sequential patterns in biological sequences. The algorithm’s processing speed is faster than that of PrefixSpan, its leading competitor, and it is superior to other sequential pattern mining algorithms for biological sequences.