Multiple Sequence Alignment Tools Research Articles

DNA barcoding, inter-specific and inter-generic variation studies on South Indian scaly tree ferns (Cyathea sp.pl.) using chloroplast rbcL gene

DNA barcoding is an effective identification tool for tree ferns with heteromorphic generations with morphologically simple gametophytic phase. A complete reference dataset which includes coverage of the target local flora is necessary for accurate identification. Three different Cyathea sp.pl. were investigated to test the utility of plastid DNA barcode regions rbcL with the intention of developing an identification system for native pteridophytes. In addition, the inter-specific and inter-generic variations among the Cyathea sp.pl. were also focused. BLAST analysis revealed the similar rbcL sequences related to C. nilgirensis Holttum, C. gigantea (Wallich ex Hook. f.) Holttum and C. crinita (Hook.) Copel and a sum of 143 sequences viz., Cyathea sp.pl. (121), Gymnosphaera sp.pl.(4), Sphaeropteris sp.pl. (1) and Alsophila sp.pl. (17). The sequences of 143 plants were retrieved from GenBank in FASTA format. The sequences were aligned using multiple sequence alignment tool MUSCLE. The inter-specific and inter-generic distance percentage were calculated using Maximum Likelihood, Minimum Evolution, UPGMA (Unweighted Pair Group Method with Arithmetic Mean) and Neighbor-Joining method. The analyses were constructed using MEGA X software. The rbcL gene provided sequence variation with its strong resolving power. This study demonstrates the overall effectiveness of DNA barcodes for species identification of tree fern genus Cyathea in the pteridophyte flora of Southern Western Ghats.

Phylogenetic Analysis and Certain Risk Factors of Chlamydia Abortus in Ewes in Nineveh Governorate, Iraq

Chlamydia is an obligate intracellular Gram-negative organism that causes abortion, or "enzootic abortion of ewes", which is characterized by causing economic loss in sheep breeding. Chlamydia abortus spread could be stopped, and monitoring efforts could be more successful by gathering data on the condition of the disease. This ground-breaking study focused on the phylogenetic analysis and certain risk factors of Chlamydia abortus in ewes in Nineveh governorate, Iraq. 52 placenta samples were collected, representing 52 flocks (8660 ewes) who suffered from abortion in various Nineveh provincial locations between August 2022 and January 2023. Two local sequences were submitted to GenBank and assigned the accession numbers OR334580 and OR334581. The Chlamydia abortus 16S rRNA sequences were analyzed using the GenomeNet multiple sequence alignment tool (CLUSTALW), an online resource. After that, the sequences were compared to other Chlamydia abortus sequences that were accessible in the GenBank using NCBI BLAST (BLASTn) of NCBI. The Tamura-Nei model in the MEGA11 software program was used to create the phylogenetic trees. Results indicate that 15.38% (8/52) of the ewes had positive results for Chlamydia abortus, which was significantly affected by the increase in herd size and interspecies farming. Two sequences were put through individual sequence analysis, which revealed new Chlamydia abortus sequences, which were recorded for the first time in the governorate of Nineveh. We concluded that a higher risk of infection occurs in farms that contain more than 100 ewes and are mixed with other species of animals.

TPMA: A two pointers meta-alignment tool to ensemble different multiple nucleic acid sequence alignments.

Accurate multiple sequence alignment (MSA) is imperative for the comprehensive analysis of biological sequences. However, a notable challenge arises as no single MSA tool consistently outperforms its counterparts across diverse datasets. Users often have to try multiple MSA tools to achieve optimal alignment results, which can be time-consuming and memory-intensive. While the overall accuracy of certain MSA results may be lower, there could be local regions with the highest alignment scores, prompting researchers to seek a tool capable of merging these locally optimal results from multiple initial alignments into a globally optimal alignment. In this study, we introduce Two Pointers Meta-Alignment (TPMA), a novel tool designed for the integration of nucleic acid sequence alignments. TPMA employs two pointers to partition the initial alignments into blocks containing identical sequence fragments. It selects blocks with the high sum of pairs (SP) scores to concatenate them into an alignment with an overall SP score superior to that of the initial alignments. Through tests on simulated and real datasets, the experimental results consistently demonstrate that TPMA outperforms M-Coffee in terms of aSP, Q, and total column (TC) scores across most datasets. Even in cases where TPMA's scores are comparable to M-Coffee, TPMA exhibits significantly lower running time and memory consumption. Furthermore, we comprehensively assessed all the MSA tools used in the experiments, considering accuracy, time, and memory consumption. We propose accurate and fast combination strategies for small and large datasets, which streamline the user tool selection process and facilitate large-scale dataset integration. The dataset and source code of TPMA are available on GitHub (https://github.com/malabz/TPMA).

PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features

Protein–peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein–peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git.

Analysis of Multiple PCR Bands in Drimia Species Obtained by R-Gene Specific Degenerate Primers

Many studies confirmed that degenerate primers LM638 and LM637 amplify NBS-LRR Type R gene encoding sequences in both monocots and dicots. In addition, these primers also amplify nonspecific multiple bands along with specific amplification. The current study aims to explore the structure, role and reason for nonspecific bands in the amplification. The genomic DNA-degenerate PCR method was used to isolate the NBS-LRR type R-genes in Drimia species (synonym Urginea). Agarose gel electrophoresis was carried out for separation and visualization of amplified bands. The kit-based gel extraction method was followed to purify the PCR bands. Purified PCR bands were subjected to TA cloning. The plasmids which contain desired PCR bands were sequenced using the Sanger sequencing method. Multiple sequence alignment tool CLUSTALW was used to align sequences and MISA-Web tool was used to identify simple sequence repeats (SSR). The results of the study revealed that multiple sequence analysis of PCR bands of 600bp/500bp showed random deletions, presence of indels and frameshift mutations in the alignment. The sequence alignment also showed monomorphic sites between two sequences. Both 600 and 500bp were devoid of simple sequence repeats and the ORF finder confirmed the presence of irregular stop codons in the 600 bp sequences. The multiple sequence alignment tool was used in disclosing the structure and ancestor relationship between aligned Drimia sequences. The current study would help to explore pseudo genes in the genome of Drimia and to differentiate the orthologous and paralogous relationships in the sequences.

PC_ali: A tool for improved multiple alignments and evolutionary inference based on a hybrid protein sequence and structure similarity score.

Evolutionary inference depends crucially on the quality of multiple sequence alignments (MSA), which is problematic for distantly related proteins. Since protein structure is more conserved than sequence, it seems natural to use structure alignments for distant homologs. However, structure alignments may not be suitable for inferring evolutionary relationships. Here we examined four protein similarity measures that depend on sequence and structure (fraction of aligned residues, sequence identity, fraction of superimposed residues and contact overlap), finding that they are intimately correlated but none of them provides a complete and unbiased picture of conservation in proteins. Therefore, we propose the new hybrid protein sequence and structure similarity score PC_sim based on their main Principal Component. The corresponding divergence measure PC_div shows the strongest correlation with divergences obtained from individual similarities, suggesting that it infers accurate evolutionary divergences. We developed the program PC_ali that constructs protein MSAs either de novo or modifying an input MSA, using a similarity matrix based on PC_sim. tructs a starting MSA based on the maximal cliques of the graph of these PAs and it refines it through progressive alignments along the tree reconstructed with PC_div. Compared with eight state-of-the-art multiple structure or sequence alignment tools, PC_ali achieve higher or equal aligned fraction and structural scores, sequence identity higher than structure aligners although lower than sequence aligners, highest score PC_sim and highest similarity with the MSAs produced by other tools and with the reference MSA Balibase. https://github.com/ugobas/PC_ali. Supplementary data are available at Bioinformatics online.

On closing the inopportune gap with consistency transformation and iterative refinement.

The problem of aligning multiple biological sequences has fascinated scientists for a long time. Over the last four decades, tens of heuristic-based Multiple Sequence Alignment (MSA) tools have been proposed, the vast majority being built on the concept of Progressive Alignment. It is known, however, that this approach suffers from an inherent drawback regarding the inadvertent insertion of gaps when aligning sequences. Two well-known corrective solutions have frequently been adopted to help mitigate this: Consistency Transformation and Iterative Refinement. This paper takes a tool-independent technique-oriented look at the alignment quality benefits of these two strategies using problem instances from the HOMSTRAD and BAliBASE benchmarks. Eighty MSA aligners have been used to compare 4 classes of heuristics: Progressive Alignments, Iterative Alignments, Consistency-based Alignments, and Consistency-based Progressive Alignments with Iterative Refinement. Statistically, while both Consistency-based classes are better for alignments with low similarity, for sequences with higher similarity, the differences between the classes are less clear. Iterative Refinement has its own drawbacks resulting in there being statistically little advantage for Progressive Aligners to adopt this technique either with Consistency Transformation or without. Nevertheless, all 4 classes are capable of bettering each other, depending on the instance problem. This further motivates the development of MSA frameworks, such as the one being developed for this research, which simultaneously contemplate multiple classes and techniques in their attempt to uncover better solutions.

Detecting gene breakpoints in noisy genome sequences using position-annotated colored de-Bruijn graphs

BackgroundIdentifying the locations of gene breakpoints between species of different taxonomic groups can provide useful insights into the underlying evolutionary processes. Given the exact locations of their genes, the breakpoints can be computed without much effort. However, often, existing gene annotations are erroneous, or only nucleotide sequences are available. Especially in mitochondrial genomes, high variations in gene orders are usually accompanied by a high degree of sequence inconsistencies. This makes accurately locating breakpoints in mitogenomic nucleotide sequences a challenging task.ResultsThis contribution presents a novel method for detecting gene breakpoints in the nucleotide sequences of complete mitochondrial genomes, taking into account possible high substitution rates. The method is implemented in the software package DeBBI. DeBBI allows to analyze transposition- and inversion-based breakpoints independently and uses a parallel program design, allowing to make use of modern multi-processor systems. Extensive tests on synthetic data sets, covering a broad range of sequence dissimilarities and different numbers of introduced breakpoints, demonstrate DeBBI ’s ability to produce accurate results. Case studies using species of various taxonomic groups further show DeBBI ’s applicability to real-life data. While (some) multiple sequence alignment tools can also be used for the task at hand, we demonstrate that especially gene breaks between short, poorly conserved tRNA genes can be detected more frequently with the proposed approach.ConclusionThe proposed method constructs a position-annotated de-Bruijn graph of the input sequences. Using a heuristic algorithm, this graph is searched for particular structures, called bulges, which may be associated with the breakpoint locations. Despite the large size of these structures, the algorithm only requires a small number of graph traversal steps.

Abalign: a comprehensive multiple sequence alignment platform for B-cell receptor immune repertoires.

The utilization of high-throughput sequencing (HTS) for B-cell receptor (BCR) immune repertoire analysis has become widespread in the fields of adaptive immunity and antibody drug development. However, the sheer volume of sequences generated by these experiments presents a challenge in data processing. Specifically, multiple sequence alignment (MSA), a critical aspect of BCR analysis, remains inadequate for handling massive BCR sequencing data and lacks the ability to provide immunoglobulin-specific information. To address this gap, we introduce Abalign, a standalone program specifically designed for ultrafast MSA of BCR/antibody sequences. Benchmark tests demonstrate that Abalign achieves comparable or even better accuracy than state-of-the-art MSA tools, and shows remarkable advantages in terms of speed and memory consumption, reducing the time required for high-throughput analysis from weeks to hours. In addition to its alignment capabilities, Abalign offers a broad range of BCR analysis features, including extracting BCRs, constructing lineage trees, assigning VJ genes, analyzing clonotypes, profiling mutations, and comparing BCR immune repertoires. With its user-friendly graphic interface, Abalign can be easily run on personal computers instead of computing clusters. Overall, Abalign is an easy-to-use and effective tool that enables researchers to analyze massive BCR/antibody sequences, leading to new discoveries in the field of immunoinformatics. The software is freely available at http://cao.labshare.cn/abalign/.

UniProt Tools: BLAST, Align, Peptide Search, and ID Mapping.

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data (UniProt Consortium, 2023). The UniProt website receives about 800,000 unique visitors per month and is the primary means to access UniProt. Along with various datasets that you can search, UniProt provides four main tools. These are the "BLAST" tool for sequence similarity searching, the "Align" tool for multiple sequence alignment, the "Peptide Search" tool for retrieving proteins containing a short peptide sequence, and the "Retrieve/ID Mapping" tool for using a list of identifiers to retrieve UniProt Knowledgebase (UniProtKB) proteins and to convert database identifiers from UniProt to external databases or vice versa. This article provides four basic protocols and seven alternate protocols for using UniProt tools. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Basic local alignment search tool (BLAST) in UniProt Alternate Protocol 1: BLAST through UniProt text search results pages Alternate Protocol 2: BLAST through UniProt basket Basic Protocol 2: Multiple sequence alignment in UniProt Alternate Protocol 3: Align tool through UniProt results pages and entry pages Alternate Protocol 4: Align tool through UniProt basket Basic Protocol 3: Peptide search in UniProt Basic Protocol 4: Batch retrieval and ID mapping in UniProt Alternate Protocol 5: Retrieve/ID Mapping tool through UniProt text search results pages and BLAST and Align results pages Alternate Protocol 6: Retrieve/ID Mapping tool through UniProt basket Alternate Protocol 7: Retrieve/ID Mapping tool through UniProt search box.

MAMMLE: A Framework for Phylogeny Estimation Based on Multiobjective Application-aware Multiple Sequence Alignment and Maximum Likelihood Ensemble.

Motivation: Phylogenetic trees are often inferred from a multiple sequence alignment (MSA) where the tree accuracy is heavily impacted by the nature of estimated alignment. Carefully equipping an MSA tool with multiple application-aware objectives positively impacts its capability to yield better trees. Results: We introduce Multiobjective Application-aware Multiple Sequence Alignment and Maximum Likelihood Ensemble (MAMMLE), a framework for inferring better phylogenetic trees from unaligned sequences by hybridizing two MSA tools [i.e., Multiple Sequence Comparison by Log-Expectation (MUSCLE) and Multiple Alignment using Fast Fourier Transform (MAFFT)] with multiobjective optimization strategy and leveraging multiple maximum likelihood hypotheses. In our experiments, MAMMLE exhibits 5.57% (4.77%) median improvement (deterioration) over MUSCLE on 50.34% (37.41%) of instances.

MLProbs: A Data-Centric Pipeline for Better Multiple Sequence Alignment.

In this paper, we explore using the data-centric approach to tackle the Multiple Sequence Alignment (MSA) construction problem. Unlike the algorithm-centric approach, which reduces the construction problem to a combinatorial optimization problem based on an abstract mathematical model, the data-centric approach explores using classification models trained from existing benchmark data to guide the construction. We identified two simple classifications to help us choose a better alignment tool and determine whether and how much to carry out realignment. We show that shallow machine-learning algorithms suffice to train sensitive models for these classifications. Based on these models, we implemented a new multiple sequence alignment pipeline, called MLProbs. Compared with 10 other popular alignment tools over four benchmark databases (namely, BAliBASE, OXBench, OXBench-X and SABMark), MLProbs consistently gives the highest TC score. More importantly, MLProbs shows non-trivial improvement for protein families with low similarity; in particular, when evaluated against the 1,356 protein families with similarity ≤ 50%, MLProbs achieves a TC score of 56.93, while the next best three tools are in the range of [55.41, 55.91] (increased by more than 1.8%). We also compared the performance of MLProbs and other MSA tools in two real-life applications - Phylogenetic Tree Construction Analysis and Protein Secondary Structure Prediction - and MLProbs also had the best performance. In our study, we used only shallow machine-learning algorithms to train our models. It would be interesting to study whether deep-learning methods can help make further improvements, so we suggest some possible research directions in the conclusion section.

Exploring Reinforcement Learning Methods for Multiple Sequence Alignment: A Brief Review

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.

Rare mutations in NLRP3 and NLRP12 associated with familial cold autoinflammatory syndrome: two Chinese pedigrees.

Familial cold autoinflammatory syndrome (FCAS) is the mildest subtype of cryopyrin-associated periodic syndrome (CAPS) and is a rare inherited systemic autoinflammatory disease (SAID). CAPS is the consequence of a rare group of genetic disorders that are mostly reported in European and American populations, but scarcely reported in Chinese populations. NLRP3, NLRP12, PLCG2, and NLRC4 are known pathogenic genes associated with FCAS, and the aim of this study was to identify pathogenic mutations in two intact pedigrees of Chinese FCAS. We performed Sanger sequencing of genomic DNA samples from 25 affected and 32 unaffected members of the two intact pedigrees and analyzed the pathogenic mutations for their conservativeness using multiple sequence alignment tools. In addition, we reviewed previously reported pathogenic genes of FCAS and their pathogenicity classification and summarized the characteristics of different genotypes and phenotypes of FCAS. This study reported two intact FCAS pedigrees with different genotypes and phenotypes, the heterozygous mutation (p.V72M) in NLRP3 in pedigree 1 and the heterozygous mutation (p.R754H) in NLRP12 in pedigree 2. There are no reports targeting p.V72M in NLRP3 in FCAS1, and there are relatively few relevant phenotypic data on the clinical manifestations identified in previous pedigrees. Multiple sequence comparisons of NLRP3 indicate that the p.V72M mutation is highly conserved during evolution. Our study has enriched the understanding of the pathogenesis of FCAS, a rare disease especially in Asian populations. KEY POINTS: •The NLRP3 (p.V72M) variant was first discovered in the Chinese pedigree of FCAS1 •NLRP12 (p.R754H) variants are not conserved in multiple sequence alignments, but they are still co-segregated and expressed in the big Chinese diseased pedigree.

Ggmsa: a visual exploration tool for multiple sequence alignment and associated data.

The identification of the conserved and variable regions in the multiple sequence alignment (MSA) is critical to accelerating the process of understanding the function of genes. MSA visualizations allow us to transform sequence features into understandable visual representations. As the sequence-structure-function relationship gains increasing attention in molecular biology studies, the simple display of nucleotide or protein sequence alignment is not satisfied. A more scalable visualization is required to broaden the scope of sequence investigation. Here we present ggmsa, an R package for mining comprehensive sequence features and integrating the associated data of MSA by a variety of display methods. To uncover sequence conservation patterns, variations and recombination at the site level, sequence bundles, sequence logos, stacked sequence alignment and comparative plots are implemented. ggmsa supports integrating the correlation of MSA sequences and their phenotypes, as well as other traits such as ancestral sequences, molecular structures, molecular functions and expression levels. We also design a new visualization method for genome alignments in multiple alignment format to explore the pattern of within and between species variation. Combining these visual representations with prime knowledge, ggmsa assists researchers in discovering MSA and making decisions. The ggmsa package is open-source software released under the Artistic-2.0 license, and it is freely available on Bioconductor (https://bioconductor.org/packages/ggmsa) and Github (https://github.com/YuLab-SMU/ggmsa).

Developments in Algorithms for Sequence Alignment: A Review.

The continuous development of sequencing technologies has enabled researchers to obtain large amounts of biological sequence data, and this has resulted in increasing demands for software that can perform sequence alignment fast and accurately. A number of algorithms and tools for sequence alignment have been designed to meet the various needs of biologists. Here, the ideas that prevail in the research of sequence alignment and some quality estimation methods for multiple sequence alignment tools are summarized.

Databases, Knowledgebases, and Software Tools for Virus Informatics.

Virus infection is a common social health issue. In the past decades, serious virus infectious events have caused great loss in people's life and the economics. The nature of rapid widespread and frequent variation increases the difficulty for precision viral prevention and treatment. In the era of big data and artificial intelligence (AI), advances in bioinformatics techniques bring unprecedented opportunities for virus informatics study, which contribute to the systems-level modeling of virus biology. In this chapter, data resources including virus-related databases and knowledgebases are introduced. Bioinformatics models and software tools for multiple sequence alignment, evolutionary analysis, and genome-wide research of viruses are summarized and emphasized. Translational applications of recently developed data-driven and AI-assisted methods to viral cases such as SARS-CoV-2, HBV/HCV, and influenza virus are discussed. Finally, the concept and significance of virus informatics are highlighted for both virus surveillance and health promotion.

Re-characterization of Potential Zoonotic Trematode Parasite Parasitizing Intestine of Snakehead Fish, (Channa striata) Utilizing Multiple Sequence Alignment Tools (MSA)

Metacercariae of Clinostomum Leidy, 1856 are frequently encountered in freshwater fish. The species configuration and taxonomic antiquity of the genus Clinostomum has been very unsteady and under discussion. Two species, i.e. Clinostomum complanatum Rudolphi, 1814 and Clinostomum marginatum Rudolphi, 1819, have been specifically troublesome and their taxonomic validation has been debated. 19 human incidences of Clinostomum complanatum have been reported to cause pharyngitis and lacramalitis from Japan, Thailand and Korea. Clinostomum marginatum is commonly known as yellow grub as it causes pinhead sized lumps in fishes that are white to yellow or black. In the present communication, we have made use of cytochrome oxidase c subunit 1 gene as DNA barcode for validating the taxonomy of C. complanatum and C. marginatum harboring phylogenetic studies and multiple sequence alignment tools. Currently, in India species of this group are distinguished mainly on the basis of morphological characters that underlie possible confusions. Therefore, we disinterred the species through molecular analysis. The current findings are salient to authenticate the phylogenetic place and relationship among two trematode spp. and bypass misidentification regarding their validation, as it is more necessary in that case when many species harbours the same host and are of zoonotic importance.

THREE MSA TOOLS ANALYSIS in DNA and PROTEIN DATASETS

Multiple sequence alignment (MSA) is used to align three or more sequences of DNA, RNA and protein. It is prominent for constructing phylogenetic trees and evolutionary relationships between sequences with regard to similarities and dissimilarities. Variety of multiple sequence alignment tools are available online, each having different methods and parameters to align sequences. In this article three MSA tools; CLUSTALW, SAGA and MAFFT are used for five datasets BALiBASE_R9, DIRMBASE, SABmark and additionally constructed DNABali and ProteinBali for alignment. Result show that for both protein and DNA dataset, MAFFT may be more useful among three of MSA tool used.

ProPIP: a tool for progressive multiple sequence alignment with Poisson Indel Process

BackgroundCurrent alignment tools typically lack an explicit model of indel evolution, leading to artificially short inferred alignments (i.e., over-alignment) due to inconsistencies between the indel history and the phylogeny relating the input sequences.ResultsWe present a new progressive multiple sequence alignment tool ProPIP. The process of insertions and deletions is described using an explicit evolutionary model—the Poisson Indel Process or PIP. The method is based on dynamic programming and is implemented in a frequentist framework. The source code can be compiled on Linux, macOS and Microsoft Windows platforms. The algorithm is implemented in C++ as standalone program. The source code is freely available on GitHub at https://github.com/acg-team/ProPIP and is distributed under the terms of the GNU GPL v3 license.ConclusionsThe use of an explicit indel evolution model allows to avoid over-alignment, to infer gaps in a phylogenetically consistent way and to make inferences about the rates of insertions and deletions. Instead of the arbitrary gap penalties, the parameters used by ProPIP are the insertion and deletion rates, which have biological interpretation and are contextualized in a probabilistic environment. As a result, indel rate settings may be optimised in order to infer phylogenetically meaningful gap patterns.