Phylogenetic analysis of some leguminous trees using CLUSTALW2 bioinformatics tool

The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.

Predicting protein structure from primary sequence is one of the ultimate challenges in computational biology. Given the large amount of available sequence data, the analysis of co-evolution, i.e., statistical dependency, between columns in multiple alignments of protein domain sequences remains one of the most promising avenues for predicting residues that are contacting in the structure. A key impediment to this approach is that strong statistical dependencies are also observed for many residue pairs that are distal in the structure. Using a comprehensive analysis of protein domains with available three-dimensional structures we show that co-evolving contacts very commonly form chains that percolate through the protein structure, inducing indirect statistical dependencies between many distal pairs of residues. We characterize the distributions of length and spatial distance traveled by these co-evolving contact chains and show that they explain a large fraction of observed statistical dependencies between structurally distal pairs. We adapt a recently developed Bayesian network model into a rigorous procedure for disentangling direct from indirect statistical dependencies, and we demonstrate that this method not only successfully accomplishes this task, but also allows contacts with weak statistical dependency to be detected. To illustrate how additional information can be incorporated into our method, we incorporate a phylogenetic correction, and we develop an informative prior that takes into account that the probability for a pair of residues to contact depends strongly on their primary-sequence distance and the amount of conservation that the corresponding columns in the multiple alignment exhibit. We show that our model including these extensions dramatically improves the accuracy of contact prediction from multiple sequence alignments.

This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA) problem. MSA is one of the most important tasks in biological sequence analysis. Although this paper focuses on protein alignments, most of the discussion and methodology may also be applied to DNA alignments. The problem of finding the multiple alignment was investigated in the study by Bonizzoni and Vedova and Wang and Jiang, and proved to be a NP-hard (non-deterministic polynomial-time hard) problem. The presented algorithm, called Immunological Multiple Sequence Alignment Algorithm (IMSA), incorporates two new strategies to create the initial population and specific ad hoc mutation operators. It is based on the ‘weighted sum of pairs’ as objective function, to evaluate a given candidate alignment. IMSA was tested using both classical benchmarks of BAliBASE (versions 1.0, 2.0 and 3.0), and experimental results indicate that it is comparable with state-of-the-art multiple alignment algorithms, in terms of quality of alignments, weighted Sums-of-Pairs (SP) and Column Score (CS) values. The main novelty of IMSA is its ability to generate more than a single suboptimal alignment, for every MSA instance; this behaviour is due to the stochastic nature of the algorithm and of the populations evolved during the convergence process. This feature will help the decision maker to assess and select a biologically relevant multiple sequence alignment. Finally, the designed algorithm can be used as a local search procedure to properly explore promising alignments of the search space.

PASTA with many application-aware optimization criteria for alignment based phylogeny inference

Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein structure prediction for decades. Recent breakthroughs like AlphaFold2 that use transformers to attend directly over large quantities of raw MSAs have reaffirmed their importance. Generation of MSAs is highly computationally intensive, however, and no datasets comparable to those used to train AlphaFold2 have been made available to the research community, hindering progress in machine learning for proteins. To remedy this problem, we introduce OpenProteinSet, an open-source corpus of more than 16 million MSAs, associated structural homologs from the Protein Data Bank, and AlphaFold2 protein structure predictions. We have previously demonstrated the utility of OpenProteinSet by successfully retraining AlphaFold2 on it. We expect OpenProteinSet to be broadly useful as training and validation data for 1) diverse tasks focused on protein structure, function, and design and 2) large-scale multimodal machine learning research.

Multiple sequence alignment (MSA) plays a vital role in uncovering similarities among biological sequences such as DNA, RNA, or proteins, providing valuable information about their structural, functional, and evolutionary relationships. However, MSA is a computationally challenging problem, with complexity growing exponentially as the number and length of sequences increase. Currently, standard MSA tools like ClustalW, T-Coffee, and MAFFT, which are based on heuristic algorithms, are widely used but still face many challenges due to the combinatorial explosion. Recent advancements in MSA algorithms have employed reinforcement learning (RL), particularly deep reinforcement learning (DRL), and demonstrated optimized execution time and accuracy with promising results. This is because deep reinforcement learning algorithms update their search policies using gradient descent, instead of exploring the entire solution space making it significantly faster and efficient. In this article, we provide an overview of the recent historical advancements in MSA algorithms, highlighting RL models used to tackle the MSA problem and main challenges and opportunities in this regard.

In this paper, we introduce an improved parallel algorithm for computing the number of exact matches nid (S,T) in the local alignment of two biological sequences S and T. This number is used in the first stage of progressive alignment to compute the distance between two sequences. The distance computations are usually its most computationally intensive part. Therefore, this work concentrates on improving an algorithm for this stage using vectorizing technique and running on multi-core. Our program is able to compute nid (S,T) between very long sequences, up to 34 k residues by C++ with OpenMP library on an Intel Core-i7-3770 quad-core processor of 3.40 GHz and main memory of 8 GB. It outperforms ClustalW-MPI 0.13 with 2.9-fold speedup, and the efficiency reached 0.35. Furthermore, a higher speedup with improved efficiency can be accomplished. Its performance figures vary from a low of 0.438 GCUPS to a high of 3.66 GCUPS as the lengths of the query sequences decrease from 34,500 to 9200. Index Terms: Bioinformatics, Distance Computation, Multi-cores, Multiple Sequence Alignment, Parallel Programming

Motivation: In the analysis of homologous sequences, computation of multiple sequence alignments (MSAs) has become a bottleneck. This is especially troublesome for marker genes like the ribosomal RNA (rRNA) where already millions of sequences are publicly available and individual studies can easily produce hundreds of thousands of new sequences. Methods have been developed to cope with such numbers, but further improvements are needed to meet accuracy requirements.Results: In this study, we present the SILVA Incremental Aligner (SINA) used to align the rRNA gene databases provided by the SILVA ribosomal RNA project. SINA uses a combination of k-mer searching and partial order alignment (POA) to maintain very high alignment accuracy while satisfying high throughput performance demands.SINA was evaluated in comparison with the commonly used high throughput MSA programs PyNAST and mothur. The three BRAliBase III benchmark MSAs could be reproduced with 99.3, 97.6 and 96.1 accuracy. A larger benchmark MSA comprising 38 772 sequences could be reproduced with 98.9 and 99.3% accuracy using reference MSAs comprising 1000 and 5000 sequences. SINA was able to achieve higher accuracy than PyNAST and mothur in all performed benchmarks.Availability: Alignment of up to 500 sequences using the latest SILVA SSU/LSU Ref datasets as reference MSA is offered at http://www.arb-silva.de/aligner. This page also links to Linux binaries, user manual and tutorial. SINA is made available under a personal use license.Contact:epruesse@mpi-bremen.deSupplementary information:Supplementary data are available at Bioinformatics online.

Bioinformatics is an extremely important field of biological science that also includes in-depth knowledge and skill in statistics and computer science. With the advance of new sequencing projects, bioinformatics helps to comprehend biological processes to primarily serve the agriculture and healthcare sectors with various spinoffs. To address the advances and awareness in bioinformatics to students and researchers this book will serve as a quick reference book on the subject. Bioinformatics is essential in all the fields of biotechnology and molecular biology that deal with molecular data. Thus the subject caters to divergent disciplines of biological science. The book on Concepts in Bioinformatics, Basics to Advances is a compilation of basic information on bioinformatics and also includes advanced areas that are required by students and professionals. The authors have great knowledge and experience in putting together updated information on animal biotechnology through eminent experts. The beginning of the book familiarizes the readers with the concept of bioinformatics and its history. Then some important concepts of basic bioinformatics have been discussed in the subsequent five chapters, that includes, databases, multiple sequence alignment, primer designing, and molecular phylogeny. This section is important to the postgraduate students of bioinformatics, biotechnology, and molecular biology. Chapters 9 to chapter 14 discusses BLAST and FASTA, protein structure prediction through homology modeling and molecular modeling, which are equally important for acquiring skills for in silico analysis. The last three chapters of the book discuss some advanced components of bioinformatics, namely, drug designing, systems biology, and synthetic biology. In general, the book is meant as a “short introduction” to bioinformatics and can be used as a sensitizer to study the subject. The book provides a comprehensive introduction to the field of bioinformatics, covering a range of topics from basic concepts to advanced techniques. The book is aimed at students, researchers, and educators in the field of biotechnology and bioinformatics, and is designed to be a valuable resource for those just starting out in the field. The topics covered in the book include molecular data analysis, multiple sequence alignment, primer design, phylogenomics, omics, molecular modeling, drug design, and synthetic biology. Overall, it seems like the book would be a useful tool for anyone looking to gain a solid foundation in the field of bioinformatics.

A multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences. Main idea behind multiple sequence alignment is to see the similarities between input sequences, to be able to make phylogenetic analysis and other evolutionary conclusions. We propose a multiple sequence alignment method based on contact maps derived from structural data and network properties. We show that such methods may be useful in creating multiple alignments that can identify domains and similar structures where sequence identity is low.

Motivation: Multiple sequence alignments (MSAs) with large numbers of sequences are now commonplace. However, current multiple alignment benchmarks are ill-suited for testing these types of alignments, as test cases either contain a very small number of sequences or are based purely on simulation rather than empirical data. Results: We take advantage of recent developments in protein structure prediction methods to create a benchmark (ContTest) for protein MSAs containing many thousands of sequences in each test case and which is based on empirical biological data. We rank popular MSA methods using this benchmark and verify a recent result showing that chained guide trees increase the accuracy of progressive alignment packages on datasets with thousands of proteins. Availability and implementation: Benchmark data and scripts are available for download at http://www.bioinf.ucd.ie/download/ContTest.tar.gz. Contact: des.higgins@ucd.ie Supplementary information: Supplementary data are available at Bioinformatics online.

The recent CASP15 competition highlighted the critical role of multiple sequence alignments (MSAs) in protein structure prediction, as demonstrated by the success of the top AlphaFold2-based prediction methods. To push the boundaries of MSA utilization, we conducted a petabase-scale search of the Sequence Read Archive (SRA), resulting in gigabytes of aligned homologs for CASP15 targets. These were merged with default MSAs produced by ColabFold-search and provided to ColabFold-predict. By using SRA data, we achieved highly accurate predictions (GDT_TS > 70) for 66% of the non-easy targets, whereas using ColabFold-search default MSAs scored highly in only 52%. Next, we tested the effect of deep homology search and ColabFold's advanced features, such as more recycles, on prediction accuracy. While SRA homologs were most significant for improving ColabFold's CASP15 ranking from 11th to 3rd place, other strategies contributed too. We analyze these in the context of existing strategies to improve prediction.

Proteins in the Genome Browser: Integration of Phylogenies, Alignments, and Structures With Nucleotide-level Evidence in JBrowse 2.

Gap costs for multiple sequence alignment

Inverse parametric sequence alignment